{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import wandb\n",
    "\n",
    "import os\n",
    "import shutil\n",
    "\n",
    "import torch\n",
    "import scipy\n",
    "import numpy as np\n",
    "\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import tabulate\n",
    "\n",
    "import itertools\n",
    "\n",
    "from matplotlib import rc\n",
    "\n",
    "rc('text', usetex=True)\n",
    "rc('font',**{'family':'serif','serif':['Computer Modern Roman']})\n",
    "\n",
    "%matplotlib inline\n",
    "palette = sns.color_palette()\n",
    "\n",
    "api = wandb.Api()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Note:** this notebook uses outdated experiment config names; you may need to change experiment name regexps to run this code."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def query_runs(api, exp_name_regex, step):    \n",
    "    runs = api.runs(\n",
    "        'timgaripov/support_alignment_v2', # Use your wandb project name heere\n",
    "        filters={\n",
    "            '$and': [\n",
    "                {'config.experiment_name': {'$regex': exp_name_regex}},\n",
    "                {'state': 'finished'},\n",
    "                {'summary_metrics.step': {'$eq': step}},\n",
    "            ]\n",
    "        },\n",
    "        order='config.config/training/seed.value'\n",
    "    )\n",
    "    return runs\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "class ResultsTable(object):\n",
    "    def mean_std_fn(vals):    \n",
    "        mean = np.mean(vals)\n",
    "        std = np.std(vals)\n",
    "        return f'{mean:.2f} ({std:.2f}) [{len(vals)}]'\n",
    "    def median_iqr_fn(vals):    \n",
    "        median = np.median(vals)\n",
    "        iqr = scipy.stats.iqr(vals)\n",
    "        return f'{median:.2f} ({iqr:.2f}) [{len(vals)}]'\n",
    "    def quant_fn(vals):    \n",
    "        quants = np.quantile(vals, np.array([0.25, 0.5, 0.75]))\n",
    "        return f'{quants[0]:05.2f} {quants[1]:05.2f} {quants[2]:05.2f} [{len(vals)}]'\n",
    "    \n",
    "    def quant_latex_fn(vals):    \n",
    "        quants = np.quantile(vals, np.array([0.25, 0.5, 0.75]))\n",
    "        return f'$ {quants[1]:04.1f}_{{~{quants[0]:04.1f}}}^{{~{quants[2]:04.1f}}} $'\n",
    "    \n",
    "    def median_x_fn(vals):    \n",
    "        median = np.median(vals)        \n",
    "        return f'{median:.4f} [{len(vals)}]'\n",
    "    \n",
    "    def median_x_fn(vals):    \n",
    "        median = np.median(vals)\n",
    "        iqr = scipy.stats.iqr(vals)\n",
    "        return f'{median:.4f} [{len(vals)}]'\n",
    "    \n",
    "    def quant_latex_x_fn(vals):    \n",
    "        quants = np.quantile(vals, np.array([0.25, 0.5, 0.75]))\n",
    "        return f'$ {quants[1]:05.2f}_{{~{quants[0]:05.2f}}}^{{~{quants[2]:05.2f}}} $'\n",
    "    \n",
    "    def __init__(self, prefix, algorithms, imbalance_levels, num_steps):\n",
    "        api = wandb.Api()\n",
    "        \n",
    "        self.prefix = prefix\n",
    "        self.imbalance_levels = imbalance_levels\n",
    "        self.algorithms = algorithms\n",
    "        \n",
    "        \n",
    "        self.summaries = [\n",
    "            'eval/target_val/accuracy_class_avg',\n",
    "            'eval/target_val/accuracy_class_min',\n",
    "            'eval/target_val/ce_class_avg',            \n",
    "            'alignment_eval_original/ot_sq',\n",
    "            'alignment_eval_original/supp_dist_sq',            \n",
    "            'alignment_eval_original/log_loss',\n",
    "        ]\n",
    "        \n",
    "        self.results = {\n",
    "            summary_name: list() for summary_name in self.summaries\n",
    "        }\n",
    "        for algorithm_name in algorithms:\n",
    "            for summary_name in self.summaries:\n",
    "                self.results[summary_name].append([])\n",
    "            for imbalance_level in imbalance_levels:\n",
    "                regex = f'{prefix}/seed_[1-5]/s_alpha_{imbalance_level}/{algorithm_name}[^0_]'\n",
    "                print(regex)\n",
    "                runs = query_runs(api, regex, step=num_steps)\n",
    "                print(f'Runs: {len(runs)}')\n",
    "                for summary_name in self.summaries:                    \n",
    "                    self.results[summary_name][-1].append([run.summary.get(summary_name, -1.0) for run in runs])                                                \n",
    "                \n",
    "            print()\n",
    "        \n",
    "    def print_table(self, summaries, agg_fn, tablefmt='pipe', sep=' '):\n",
    "        print(f'{self.prefix} {\" \".join(summaries)}')\n",
    "        columns = ['algorithm'] + self.imbalance_levels\n",
    "        table = [columns]\n",
    "        for i, algorithm_name in enumerate(self.algorithms):\n",
    "            table.append([algorithm_name])\n",
    "            for j, imbalance_level in enumerate(self.imbalance_levels):\n",
    "                cell = ''\n",
    "                for k, summary_name in enumerate(summaries):\n",
    "                    values = self.results[summary_name][i][j]                \n",
    "                    if len(values) > 0:\n",
    "                        cell += agg_fn(np.array(values))\n",
    "                    if k + 1 < len(summaries):\n",
    "                        cell += sep\n",
    "                table[-1].append(cell)\n",
    "        print(tabulate.tabulate(table, tablefmt=tablefmt, headers=\"firstrow\"))\n",
    "        "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# USPS->MNIST main results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0_usps_mnist/test_lenet_v3/seed_[1-5]/s_alpha_00/dann_zero[^0_]\n",
      "Runs: 5\n",
      "0_usps_mnist/test_lenet_v3/seed_[1-5]/s_alpha_10/dann_zero[^0_]\n",
      "Runs: 5\n",
      "0_usps_mnist/test_lenet_v3/seed_[1-5]/s_alpha_15/dann_zero[^0_]\n",
      "Runs: 5\n",
      "0_usps_mnist/test_lenet_v3/seed_[1-5]/s_alpha_20/dann_zero[^0_]\n",
      "Runs: 5\n",
      "\n",
      "0_usps_mnist/test_lenet_v3/seed_[1-5]/s_alpha_00/dann[^0_]\n",
      "Runs: 5\n",
      "0_usps_mnist/test_lenet_v3/seed_[1-5]/s_alpha_10/dann[^0_]\n",
      "Runs: 5\n",
      "0_usps_mnist/test_lenet_v3/seed_[1-5]/s_alpha_15/dann[^0_]\n",
      "Runs: 5\n",
      "0_usps_mnist/test_lenet_v3/seed_[1-5]/s_alpha_20/dann[^0_]\n",
      "Runs: 5\n",
      "\n",
      "0_usps_mnist/test_lenet_v3/seed_[1-5]/s_alpha_00/vada[^0_]\n",
      "Runs: 5\n",
      "0_usps_mnist/test_lenet_v3/seed_[1-5]/s_alpha_10/vada[^0_]\n",
      "Runs: 5\n",
      "0_usps_mnist/test_lenet_v3/seed_[1-5]/s_alpha_15/vada[^0_]\n",
      "Runs: 5\n",
      "0_usps_mnist/test_lenet_v3/seed_[1-5]/s_alpha_20/vada[^0_]\n",
      "Runs: 5\n",
      "\n",
      "0_usps_mnist/test_lenet_v3/seed_[1-5]/s_alpha_00/iwdan_5000[^0_]\n",
      "Runs: 5\n",
      "0_usps_mnist/test_lenet_v3/seed_[1-5]/s_alpha_10/iwdan_5000[^0_]\n",
      "Runs: 5\n",
      "0_usps_mnist/test_lenet_v3/seed_[1-5]/s_alpha_15/iwdan_5000[^0_]\n",
      "Runs: 5\n",
      "0_usps_mnist/test_lenet_v3/seed_[1-5]/s_alpha_20/iwdan_5000[^0_]\n",
      "Runs: 5\n",
      "\n",
      "0_usps_mnist/test_lenet_v3/seed_[1-5]/s_alpha_00/iwcdan_5000[^0_]\n",
      "Runs: 5\n",
      "0_usps_mnist/test_lenet_v3/seed_[1-5]/s_alpha_10/iwcdan_5000[^0_]\n",
      "Runs: 5\n",
      "0_usps_mnist/test_lenet_v3/seed_[1-5]/s_alpha_15/iwcdan_5000[^0_]\n",
      "Runs: 5\n",
      "0_usps_mnist/test_lenet_v3/seed_[1-5]/s_alpha_20/iwcdan_5000[^0_]\n",
      "Runs: 5\n",
      "\n",
      "0_usps_mnist/test_lenet_v3/seed_[1-5]/s_alpha_00/sdann_4[^0_]\n",
      "Runs: 5\n",
      "0_usps_mnist/test_lenet_v3/seed_[1-5]/s_alpha_10/sdann_4[^0_]\n",
      "Runs: 5\n",
      "0_usps_mnist/test_lenet_v3/seed_[1-5]/s_alpha_15/sdann_4[^0_]\n",
      "Runs: 5\n",
      "0_usps_mnist/test_lenet_v3/seed_[1-5]/s_alpha_20/sdann_4[^0_]\n",
      "Runs: 5\n",
      "\n",
      "0_usps_mnist/test_lenet_v3/seed_[1-5]/s_alpha_00/support_sq[^0_]\n",
      "Runs: 5\n",
      "0_usps_mnist/test_lenet_v3/seed_[1-5]/s_alpha_10/support_sq[^0_]\n",
      "Runs: 5\n",
      "0_usps_mnist/test_lenet_v3/seed_[1-5]/s_alpha_15/support_sq[^0_]\n",
      "Runs: 5\n",
      "0_usps_mnist/test_lenet_v3/seed_[1-5]/s_alpha_20/support_sq[^0_]\n",
      "Runs: 5\n",
      "\n",
      "0_usps_mnist/test_lenet_v3/seed_[1-5]/s_alpha_00/support_abs[^0_]\n",
      "Runs: 5\n",
      "0_usps_mnist/test_lenet_v3/seed_[1-5]/s_alpha_10/support_abs[^0_]\n",
      "Runs: 5\n",
      "0_usps_mnist/test_lenet_v3/seed_[1-5]/s_alpha_15/support_abs[^0_]\n",
      "Runs: 5\n",
      "0_usps_mnist/test_lenet_v3/seed_[1-5]/s_alpha_20/support_abs[^0_]\n",
      "Runs: 5\n",
      "\n"
     ]
    }
   ],
   "source": [
    "prefix = '0_usps_mnist/test_lenet_v3' # change to usps_mnist/lenet\n",
    "imbalance_levels = ['00', '10', '15', '20']\n",
    "algorithms = [\n",
    "    'dann_zero',\n",
    "    'dann',    \n",
    "    'vada',\n",
    "    'iwdan_5000', # change to iwdan\n",
    "    'iwcdan_5000', # change to iwcdan\n",
    "    'sdann_4',             \n",
    "    'support_sq',\n",
    "    'support_abs',    \n",
    "]\n",
    "\n",
    "results = ResultsTable(prefix, algorithms, imbalance_levels, num_steps=65000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0_usps_mnist/test_lenet_v3 eval/target_val/accuracy_class_avg\n",
      "| algorithm   | 00               | 10               | 15               | 20               |\n",
      "|:------------|:-----------------|:-----------------|:-----------------|:-----------------|\n",
      "| dann_zero   | 71.09 (2.05) [5] | 72.95 (2.49) [5] | 71.80 (1.04) [5] | 71.75 (1.46) [5] |\n",
      "| dann        | 97.70 (0.12) [5] | 80.95 (4.84) [5] | 68.65 (6.85) [5] | 58.25 (6.37) [5] |\n",
      "| vada        | 97.96 (0.10) [5] | 88.79 (1.10) [5] | 76.89 (6.31) [5] | 61.72 (7.35) [5] |\n",
      "| iwdan_5000  | 97.45 (0.12) [5] | 94.54 (1.95) [5] | 83.78 (5.03) [5] | 74.37 (3.98) [5] |\n",
      "| iwcdan_5000 | 97.96 (0.17) [5] | 95.44 (2.31) [5] | 89.94 (5.14) [5] | 79.04 (4.13) [5] |\n",
      "| sdann_4     | 90.63 (4.18) [5] | 93.03 (3.90) [5] | 87.15 (1.81) [5] | 82.35 (2.49) [5] |\n",
      "| support_sq  | 93.61 (0.36) [5] | 92.48 (1.00) [5] | 90.38 (1.90) [5] | 87.41 (1.72) [5] |\n",
      "| support_abs | 94.06 (0.50) [5] | 91.27 (2.56) [5] | 91.74 (1.83) [5] | 89.36 (2.49) [5] |\n"
     ]
    }
   ],
   "source": [
    "results.print_table(['eval/target_val/accuracy_class_avg'], ResultsTable.mean_std_fn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0_usps_mnist/test_lenet_v3 eval/target_val/accuracy_class_avg\n",
      "| algorithm   | 00                    | 10                    | 15                    | 20                    |\n",
      "|:------------|:----------------------|:----------------------|:----------------------|:----------------------|\n",
      "| dann_zero   | 70.44 71.93 72.85 [5] | 72.05 72.91 74.70 [5] | 71.25 71.28 72.51 [5] | 70.58 71.34 72.99 [5] |\n",
      "| dann        | 97.62 97.77 97.78 [5] | 76.72 83.50 84.55 [5] | 63.89 69.96 71.25 [5] | 52.01 57.77 60.40 [5] |\n",
      "| vada        | 97.87 97.99 98.00 [5] | 88.07 88.22 89.87 [5] | 70.72 78.19 83.06 [5] | 56.27 61.86 65.38 [5] |\n",
      "| iwdan_5000  | 97.43 97.52 97.52 [5] | 92.64 95.71 95.78 [5] | 80.23 86.47 87.75 [5] | 70.01 74.40 78.57 [5] |\n",
      "| iwcdan_5000 | 97.88 97.98 98.07 [5] | 93.33 96.67 97.53 [5] | 90.46 91.32 93.84 [5] | 77.25 77.49 82.31 [5] |\n",
      "| sdann_4     | 87.20 87.39 95.68 [5] | 94.72 94.90 94.93 [5] | 85.54 86.80 89.06 [5] | 81.29 81.53 83.06 [5] |\n",
      "| support_sq  | 93.31 93.66 93.87 [5] | 91.50 92.30 93.61 [5] | 89.64 90.87 92.11 [5] | 85.78 87.22 89.25 [5] |\n",
      "| support_abs | 93.77 94.10 94.50 [5] | 89.25 92.76 93.16 [5] | 90.90 92.54 92.93 [5] | 89.21 90.43 90.69 [5] |\n"
     ]
    }
   ],
   "source": [
    "results.print_table(['eval/target_val/accuracy_class_avg'], ResultsTable.quant_fn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0_usps_mnist/test_lenet_v3 eval/target_val/accuracy_class_min\n",
      "| algorithm   | 00                | 10                | 15                | 20                |\n",
      "|:------------|:------------------|:------------------|:------------------|:------------------|\n",
      "| dann_zero   | 19.52 (4.51) [5]  | 24.96 (9.66) [5]  | 29.22 (7.94) [5]  | 18.70 (7.89) [5]  |\n",
      "| dann        | 96.06 (0.51) [5]  | 22.30 (14.09) [5] | 6.19 (10.07) [5]  | 1.08 (0.63) [5]   |\n",
      "| vada        | 96.14 (0.25) [5]  | 48.82 (3.62) [5]  | 12.37 (11.59) [5] | 1.38 (0.87) [5]   |\n",
      "| iwdan_5000  | 95.75 (0.19) [5]  | 73.42 (16.40) [5] | 27.91 (26.17) [5] | 17.12 (16.02) [5] |\n",
      "| iwcdan_5000 | 96.59 (0.42) [5]  | 76.47 (20.07) [5] | 58.19 (30.08) [5] | 24.90 (21.45) [5] |\n",
      "| sdann_4     | 40.00 (42.19) [5] | 71.88 (31.68) [5] | 35.32 (23.36) [5] | 41.11 (16.72) [5] |\n",
      "| support_sq  | 88.26 (2.89) [5]  | 83.44 (5.52) [5]  | 68.96 (15.43) [5] | 58.03 (12.58) [5] |\n",
      "| support_abs | 88.68 (2.66) [5]  | 69.69 (20.05) [5] | 74.95 (15.40) [5] | 65.48 (13.34) [5] |\n"
     ]
    }
   ],
   "source": [
    "results.print_table(['eval/target_val/accuracy_class_min'], ResultsTable.mean_std_fn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0_usps_mnist/test_lenet_v3 eval/target_val/accuracy_class_min\n",
      "| algorithm   | 00                    | 10                    | 15                    | 20                    |\n",
      "|:------------|:----------------------|:----------------------|:----------------------|:----------------------|\n",
      "| dann_zero   | 17.62 20.28 22.89 [5] | 18.33 25.85 31.82 [5] | 24.21 27.46 37.26 [5] | 10.83 16.55 26.77 [5] |\n",
      "| dann        | 95.83 96.03 96.12 [5] | 08.38 25.07 36.93 [5] | 00.99 01.11 01.53 [5] | 00.48 00.93 01.63 [5] |\n",
      "| vada        | 95.87 96.22 96.32 [5] | 47.76 48.91 50.00 [5] | 02.44 06.56 23.46 [5] | 00.83 01.36 01.54 [5] |\n",
      "| iwdan_5000  | 95.65 95.66 95.88 [5] | 67.12 81.33 82.34 [5] | 04.15 15.19 55.05 [5] | 06.31 07.32 22.36 [5] |\n",
      "| iwcdan_5000 | 96.36 96.64 96.90 [5] | 65.35 85.10 93.85 [5] | 64.07 66.49 74.50 [5] | 02.68 22.24 45.39 [5] |\n",
      "| sdann_4     | 05.58 05.63 90.01 [5] | 84.36 85.70 87.69 [5] | 15.38 21.59 50.27 [5] | 37.92 39.34 56.24 [5] |\n",
      "| support_sq  | 88.43 89.24 89.39 [5] | 80.82 83.51 88.75 [5] | 66.56 69.89 82.05 [5] | 46.42 62.49 69.34 [5] |\n",
      "| support_abs | 86.98 88.94 91.24 [5] | 65.10 78.94 82.93 [5] | 74.53 82.41 85.43 [5] | 67.55 68.36 73.00 [5] |\n"
     ]
    }
   ],
   "source": [
    "results.print_table(['eval/target_val/accuracy_class_min'], ResultsTable.quant_fn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0_usps_mnist/test_lenet_v3 eval/target_val/accuracy_class_avg eval/target_val/accuracy_class_min\n",
      "\\begin{tabular}{lllll}\n",
      "\\hline\n",
      " algorithm   & 00                                                  & 10                                                  & 15                                                  & 20                                                  \\\\\n",
      "\\hline\n",
      " dann_zero   & $ 71.9_{~70.4}^{~72.9} $ & $ 20.3_{~17.6}^{~22.9} $ & $ 72.9_{~72.0}^{~74.7} $ & $ 25.8_{~18.3}^{~31.8} $ & $ 71.3_{~71.2}^{~72.5} $ & $ 27.5_{~24.2}^{~37.3} $ & $ 71.3_{~70.6}^{~73.0} $ & $ 16.6_{~10.8}^{~26.8} $ \\\\\n",
      " dann        & $ 97.8_{~97.6}^{~97.8} $ & $ 96.0_{~95.8}^{~96.1} $ & $ 83.5_{~76.7}^{~84.6} $ & $ 25.1_{~08.4}^{~36.9} $ & $ 70.0_{~63.9}^{~71.2} $ & $ 01.1_{~01.0}^{~01.5} $ & $ 57.8_{~52.0}^{~60.4} $ & $ 00.9_{~00.5}^{~01.6} $ \\\\\n",
      " vada        & $ 98.0_{~97.9}^{~98.0} $ & $ 96.2_{~95.9}^{~96.3} $ & $ 88.2_{~88.1}^{~89.9} $ & $ 48.9_{~47.8}^{~50.0} $ & $ 78.2_{~70.7}^{~83.1} $ & $ 06.6_{~02.4}^{~23.5} $ & $ 61.9_{~56.3}^{~65.4} $ & $ 01.4_{~00.8}^{~01.5} $ \\\\\n",
      " iwdan_5000  & $ 97.5_{~97.4}^{~97.5} $ & $ 95.7_{~95.7}^{~95.9} $ & $ 95.7_{~92.6}^{~95.8} $ & $ 81.3_{~67.1}^{~82.3} $ & $ 86.5_{~80.2}^{~87.8} $ & $ 15.2_{~04.2}^{~55.0} $ & $ 74.4_{~70.0}^{~78.6} $ & $ 07.3_{~06.3}^{~22.4} $ \\\\\n",
      " iwcdan_5000 & $ 98.0_{~97.9}^{~98.1} $ & $ 96.6_{~96.4}^{~96.9} $ & $ 96.7_{~93.3}^{~97.5} $ & $ 85.1_{~65.3}^{~93.9} $ & $ 91.3_{~90.5}^{~93.8} $ & $ 66.5_{~64.1}^{~74.5} $ & $ 77.5_{~77.3}^{~82.3} $ & $ 22.2_{~02.7}^{~45.4} $ \\\\\n",
      " sdann_4     & $ 87.4_{~87.2}^{~95.7} $ & $ 05.6_{~05.6}^{~90.0} $ & $ 94.9_{~94.7}^{~94.9} $ & $ 85.7_{~84.4}^{~87.7} $ & $ 86.8_{~85.5}^{~89.1} $ & $ 21.6_{~15.4}^{~50.3} $ & $ 81.5_{~81.3}^{~83.1} $ & $ 39.3_{~37.9}^{~56.2} $ \\\\\n",
      " support_sq  & $ 93.7_{~93.3}^{~93.9} $ & $ 89.2_{~88.4}^{~89.4} $ & $ 92.3_{~91.5}^{~93.6} $ & $ 83.5_{~80.8}^{~88.7} $ & $ 90.9_{~89.6}^{~92.1} $ & $ 69.9_{~66.6}^{~82.0} $ & $ 87.2_{~85.8}^{~89.3} $ & $ 62.5_{~46.4}^{~69.3} $ \\\\\n",
      " support_abs & $ 94.1_{~93.8}^{~94.5} $ & $ 88.9_{~87.0}^{~91.2} $ & $ 92.8_{~89.3}^{~93.2} $ & $ 78.9_{~65.1}^{~82.9} $ & $ 92.5_{~90.9}^{~92.9} $ & $ 82.4_{~74.5}^{~85.4} $ & $ 90.4_{~89.2}^{~90.7} $ & $ 68.4_{~67.5}^{~73.0} $ \\\\\n",
      "\\hline\n",
      "\\end{tabular}\n"
     ]
    }
   ],
   "source": [
    "results.print_table(['eval/target_val/accuracy_class_avg', 'eval/target_val/accuracy_class_min'], \n",
    "                    ResultsTable.quant_latex_fn, tablefmt='latex_raw', sep=' & ')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# USPS->MNIST history size ablation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0_usps_mnist/test_lenet_v3/seed_[1-5]/s_alpha_15/dann_zero[^0_]\n",
      "Runs: 5\n",
      "0_usps_mnist/test_lenet_v3/seed_[1-5]/s_alpha_15/dann[^0_]\n",
      "Runs: 5\n",
      "0_usps_mnist/test_lenet_v3/seed_[1-5]/s_alpha_15/support_abs_h0[^0_]\n",
      "Runs: 5\n",
      "0_usps_mnist/test_lenet_v3/seed_[1-5]/s_alpha_15/support_abs_h100[^0_]\n",
      "Runs: 5\n",
      "0_usps_mnist/test_lenet_v3/seed_[1-5]/s_alpha_15/support_abs_h500[^0_]\n",
      "Runs: 5\n",
      "0_usps_mnist/test_lenet_v3/seed_[1-5]/s_alpha_15/support_abs[^0_]\n",
      "Runs: 5\n",
      "0_usps_mnist/test_lenet_v3/seed_[1-5]/s_alpha_15/support_abs_h5000[^0_]\n",
      "Runs: 5\n"
     ]
    }
   ],
   "source": [
    "api = wandb.Api()\n",
    "\n",
    "prefix = '0_usps_mnist/test_lenet_v3' # change to usps_mnist/lenet\n",
    "imbalance_levels = ['15']\n",
    "algorithms = [\n",
    "    'dann_zero',\n",
    "    'dann',    \n",
    "    'support_abs_h0',\n",
    "    'support_abs_h100',\n",
    "    'support_abs_h500',\n",
    "    'support_abs',    \n",
    "    'support_abs_h5000'\n",
    "]\n",
    "\n",
    "results_table = [\n",
    "    ['algorithm', 'avg acc', 'min acc', 'supp_sq', 'ot_sq', 'disc log loss', 'disc range'],\n",
    "]\n",
    "\n",
    "\n",
    "for algorithm_name in algorithms:\n",
    "    results_table.append([algorithm_name])\n",
    "    for imbalance_level in imbalance_levels:            \n",
    "        regex = f'{prefix}/seed_[1-5]/s_alpha_{imbalance_level}/{algorithm_name}[^0_]'                \n",
    "        print(regex)\n",
    "        runs = query_runs(api, regex, step=65000)\n",
    "        print(f'Runs: {len(runs)}')        \n",
    "        \n",
    "        if not runs:                \n",
    "            avg_acc_table[-1].append('')\n",
    "            min_acc_table[-1].append('')\n",
    "            continue\n",
    "                        \n",
    "        seeds = set([run.config['config/training/seed'] for run in runs])        \n",
    "        \n",
    "        avg_acc_vals = [run.summary['eval/target_val/accuracy_class_avg'] for run in runs]\n",
    "        min_acc_vals = [run.summary['eval/target_val/accuracy_class_min'] for run in runs]\n",
    "        \n",
    "        avg_acc_mean = np.mean(avg_acc_vals)\n",
    "        avg_acc_std = np.std(avg_acc_vals)\n",
    "        \n",
    "        min_acc_mean = np.mean(min_acc_vals)\n",
    "        min_acc_std = np.std(min_acc_vals)\n",
    "        \n",
    "        results_table[-1].append(f'{avg_acc_mean:05.2f} ({avg_acc_std:.2f}) [{len(runs)}]')\n",
    "        results_table[-1].append(f'{min_acc_mean:05.2f} ({min_acc_std:.2f}) [{len(runs)}]')\n",
    "        \n",
    "        disc_log_loss_vals = [run.summary['alignment_eval_original/log_loss'] for run in runs]\n",
    "        supp_sq_vals = [run.summary['alignment_eval_original/supp_dist_sq'] for run in runs]\n",
    "        ot_sq_vals = [run.summary['alignment_eval_original/ot_sq'] for run in runs]\n",
    "        disc_range_valse = [run.summary['alignment_eval_original/range'] for run in runs]\n",
    "                      \n",
    "        disc_log_loss_mean = np.mean(disc_log_loss_vals)\n",
    "        disc_log_loss_std = np.std(disc_log_loss_vals)\n",
    "                      \n",
    "        supp_sq_mean = np.mean(supp_sq_vals)\n",
    "        supp_sq_std = np.std(supp_sq_vals)\n",
    "                      \n",
    "        ot_sq_mean = np.mean(ot_sq_vals)\n",
    "        ot_sq_std = np.std(ot_sq_vals)\n",
    "        \n",
    "        disc_range_mean = np.mean(disc_range_valse)\n",
    "        disc_range_std = np.std(disc_range_valse)\n",
    "                              \n",
    "                      \n",
    "        results_table[-1].append(f'{supp_sq_mean:.2f} ({supp_sq_std:.2f})')\n",
    "        results_table[-1].append(f'{ot_sq_mean:.2f} ({ot_sq_std:.2f})')\n",
    "        results_table[-1].append(f'{disc_log_loss_mean:.2f} ({disc_log_loss_std:.2f})')        \n",
    "     "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0_usps_mnist/test_lenet_v3\n",
      "| algorithm         | avg acc          | min acc           | supp_sq       | ot_sq          | disc log loss   |\n",
      "|:------------------|:-----------------|:------------------|:--------------|:---------------|:----------------|\n",
      "| dann_zero         | 71.80 (1.04) [5] | 29.22 (7.94) [5]  | 41.10 (14.57) | 299.71 (34.21) | 0.05 (0.02)     |\n",
      "| dann              | 68.65 (6.85) [5] | 06.19 (10.07) [5] | 0.00 (0.00)   | 0.11 (0.01)    | 0.65 (0.00)     |\n",
      "| support_abs_h0    | 63.33 (1.86) [5] | 21.15 (6.30) [5]  | 0.01 (0.00)   | 1.11 (0.17)    | 0.57 (0.01)     |\n",
      "| support_abs_h100  | 79.68 (2.66) [5] | 39.78 (9.23) [5]  | 0.00 (0.00)   | 2.47 (0.27)    | 0.53 (0.01)     |\n",
      "| support_abs_h500  | 91.56 (1.94) [5] | 78.20 (8.03) [5]  | 0.00 (0.00)   | 6.54 (1.36)    | 0.45 (0.00)     |\n",
      "| support_abs       | 91.74 (1.83) [5] | 74.95 (15.40) [5] | 0.01 (0.01)   | 8.11 (0.37)    | 0.41 (0.01)     |\n",
      "| support_abs_h5000 | 86.04 (1.35) [5] | 57.48 (14.55) [5] | 0.07 (0.03)   | 27.35 (3.61)   | 0.29 (0.01)     |\n"
     ]
    }
   ],
   "source": [
    "print(prefix)\n",
    "print(tabulate.tabulate(results_table, tablefmt='pipe', headers=\"firstrow\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0_usps_mnist/test_lenet_v3/seed_[1-5]/s_alpha_15/dann_zero[^0_]\n",
      "Runs: 5\n",
      "\n",
      "0_usps_mnist/test_lenet_v3/seed_[1-5]/s_alpha_15/dann[^0_]\n",
      "Runs: 5\n",
      "\n",
      "0_usps_mnist/test_lenet_v3/seed_[1-5]/s_alpha_15/support_abs_h0[^0_]\n",
      "Runs: 5\n",
      "\n",
      "0_usps_mnist/test_lenet_v3/seed_[1-5]/s_alpha_15/support_abs_h100[^0_]\n",
      "Runs: 5\n",
      "\n",
      "0_usps_mnist/test_lenet_v3/seed_[1-5]/s_alpha_15/support_abs_h500[^0_]\n",
      "Runs: 5\n",
      "\n",
      "0_usps_mnist/test_lenet_v3/seed_[1-5]/s_alpha_15/support_abs[^0_]\n",
      "Runs: 5\n",
      "\n",
      "0_usps_mnist/test_lenet_v3/seed_[1-5]/s_alpha_15/support_abs_h2000[^0_]\n",
      "Runs: 5\n",
      "\n",
      "0_usps_mnist/test_lenet_v3/seed_[1-5]/s_alpha_15/support_abs_h5000[^0_]\n",
      "Runs: 5\n",
      "\n"
     ]
    }
   ],
   "source": [
    "prefix = '0_usps_mnist/test_lenet_v3' # change to usps_mnist/lenet\n",
    "imbalance_levels = ['15']\n",
    "algorithms = [\n",
    "    'dann_zero',\n",
    "    'dann',    \n",
    "    'support_abs_h0',\n",
    "    'support_abs_h100',\n",
    "    'support_abs_h500',\n",
    "    'support_abs',    \n",
    "    'support_abs_h2000',\n",
    "    'support_abs_h5000'\n",
    "]\n",
    "\n",
    "\n",
    "results = ResultsTable(prefix, algorithms, imbalance_levels, num_steps=65000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0_usps_mnist/test_lenet_v3 eval/target_val/accuracy_class_avg eval/target_val/accuracy_class_min alignment_eval_original/ot_sq alignment_eval_original/supp_dist_sq alignment_eval_original/log_loss\n",
      "| algorithm         | 15                                                                  |\n",
      "|:------------------|:--------------------------------------------------------------------|\n",
      "| dann_zero         | 71.2800 [5] & 27.4621 [5] & 307.5606 [5] & 40.3524 [5] & 0.0526 [5] |\n",
      "| dann              | 69.9638 [5] & 1.1070 [5] & 0.1099 [5] & 0.0005 [5] & 0.6519 [5]     |\n",
      "| support_abs_h0    | 62.7497 [5] & 19.3617 [5] & 1.0688 [5] & 0.0052 [5] & 0.5670 [5]    |\n",
      "| support_abs_h100  | 80.5766 [5] & 35.0943 [5] & 2.6446 [5] & 0.0019 [5] & 0.5262 [5]    |\n",
      "| support_abs_h500  | 92.0177 [5] & 76.9554 [5] & 6.2123 [5] & 0.0047 [5] & 0.4476 [5]    |\n",
      "| support_abs       | 92.5434 [5] & 82.4142 [5] & 8.0561 [5] & 0.0149 [5] & 0.4117 [5]    |\n",
      "| support_abs_h2000 | 92.2964 [5] & 85.1135 [5] & 12.4314 [5] & 0.0277 [5] & 0.3715 [5]   |\n",
      "| support_abs_h5000 | 86.0340 [5] & 62.1928 [5] & 29.2264 [5] & 0.0543 [5] & 0.2934 [5]   |\n"
     ]
    }
   ],
   "source": [
    "results.print_table(['eval/target_val/accuracy_class_avg', 'eval/target_val/accuracy_class_min',\n",
    "                     'alignment_eval_original/ot_sq', 'alignment_eval_original/supp_dist_sq',            \n",
    "                     'alignment_eval_original/log_loss'], \n",
    "                    ResultsTable.median_x_fn, tablefmt='pipe', sep=' & ')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0_usps_mnist/test_lenet_v3 eval/target_val/accuracy_class_avg eval/target_val/accuracy_class_min alignment_eval_original/ot_sq alignment_eval_original/supp_dist_sq alignment_eval_original/log_loss\n",
      "\\begin{tabular}{ll}\n",
      "\\hline\n",
      " algorithm         & 15                                                                                                                                                     \\\\\n",
      "\\hline\n",
      " dann_zero         & $ 71.28_{~71.25}^{~72.51} $ & $ 27.46_{~24.21}^{~37.26} $ & $ 307.56_{~277.33}^{~322.00} $ & $ 40.35_{~32.10}^{~46.06} $ & $ 00.05_{~00.04}^{~00.07} $ \\\\\n",
      " dann              & $ 69.96_{~63.89}^{~71.25} $ & $ 01.11_{~00.99}^{~01.53} $ & $ 00.11_{~00.10}^{~00.11} $ & $ 00.00_{~00.00}^{~00.00} $ & $ 00.65_{~00.65}^{~00.65} $    \\\\\n",
      " support_abs_h0    & $ 62.75_{~61.78}^{~64.35} $ & $ 19.36_{~17.90}^{~23.63} $ & $ 01.07_{~00.99}^{~01.15} $ & $ 00.01_{~00.00}^{~00.01} $ & $ 00.57_{~00.56}^{~00.58} $    \\\\\n",
      " support_abs_h100  & $ 80.58_{~78.22}^{~81.73} $ & $ 35.09_{~32.10}^{~44.37} $ & $ 02.64_{~02.15}^{~02.70} $ & $ 00.00_{~00.00}^{~00.00} $ & $ 00.53_{~00.52}^{~00.53} $    \\\\\n",
      " support_abs_h500  & $ 92.02_{~90.56}^{~92.76} $ & $ 76.96_{~70.94}^{~83.72} $ & $ 06.21_{~05.69}^{~06.48} $ & $ 00.00_{~00.00}^{~00.01} $ & $ 00.45_{~00.45}^{~00.45} $    \\\\\n",
      " support_abs       & $ 92.54_{~90.90}^{~92.93} $ & $ 82.41_{~74.53}^{~85.43} $ & $ 08.06_{~07.97}^{~08.19} $ & $ 00.01_{~00.01}^{~00.02} $ & $ 00.41_{~00.40}^{~00.41} $    \\\\\n",
      " support_abs_h2000 & $ 92.30_{~91.22}^{~92.81} $ & $ 85.11_{~82.99}^{~85.18} $ & $ 12.43_{~12.11}^{~13.35} $ & $ 00.03_{~00.03}^{~00.03} $ & $ 00.37_{~00.37}^{~00.37} $    \\\\\n",
      " support_abs_h5000 & $ 86.03_{~84.86}^{~87.50} $ & $ 62.19_{~46.98}^{~71.62} $ & $ 29.23_{~24.54}^{~29.63} $ & $ 00.05_{~00.05}^{~00.08} $ & $ 00.29_{~00.29}^{~00.30} $    \\\\\n",
      "\\hline\n",
      "\\end{tabular}\n"
     ]
    }
   ],
   "source": [
    "results.print_table(['eval/target_val/accuracy_class_avg', 'eval/target_val/accuracy_class_min',\n",
    "                     'alignment_eval_original/ot_sq', 'alignment_eval_original/supp_dist_sq',            \n",
    "                     'alignment_eval_original/log_loss'], \n",
    "                    ResultsTable.quant_latex_x_fn, tablefmt='latex_raw', sep=' & ')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "findfont: Font family ['serif'] not found. Falling back to DejaVu Sans.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 3600x669.6 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "hpalette = sns.color_palette('deep')\n",
    "\n",
    "h_points = np.array([0, 100, 500, 1000, 2000, 5000])\n",
    "x_points = np.arange(h_points.size)\n",
    "\n",
    "min_acc = np.array([19.3617, 35.0943, 76.9554, 82.4142, 85.1135, 62.1928])\n",
    "avg_acc = np.array([62.7497, 80.5766, 92.0177, 92.5434, 92.2964, 86.0340])\n",
    "min_err = 100.0 - min_acc\n",
    "avg_err = 100.0 - avg_acc\n",
    "dist_sq = np.array([1.0688, 2.6446, 6.2123, 8.0561, 12.4314, 29.2264])\n",
    "supp_sq = np.array([0.0052, 0.0019, 0.0047, 0.0149, 0.0277, 0.0543])\n",
    "\n",
    "dann_avg_acc = 69.9638\n",
    "dann_min_acc = 1.1070\n",
    "dann_dist_sq = 0.1099\n",
    "dann_supp_sq = 0.0005\n",
    "\n",
    "noda_avg_acc = 71.2800\n",
    "noda_min_acc =  27.4621\n",
    "noda_dist_sq = 307.5606\n",
    "noda_supp_sq = 40.3524\n",
    "\n",
    "fig, axes = plt.subplots(figsize=(50, 9.3), nrows=1, ncols=3)\n",
    "\n",
    "\n",
    "plt.sca(axes[0])\n",
    "\n",
    "\n",
    "c_min = hpalette[3]\n",
    "plt.axhline(y=dann_min_acc, c=c_min, linewidth=8, linestyle='--', dashes=(7, 5))\n",
    "plt.axhline(y=noda_min_acc, c=c_min, linewidth=8, linestyle='dotted')\n",
    "plt.plot(x_points, min_acc, c=c_min, linewidth=8, marker='o', markersize=20)\n",
    "\n",
    "\n",
    "plt.xticks(x_points, h_points, fontsize=50)\n",
    "plt.yticks(fontsize=50)\n",
    "plt.margins(x=0.1)\n",
    "plt.grid(linestyle='--', dashes=(16, 16))  \n",
    "plt.xlabel('ASA history size', fontsize=60)\n",
    "plt.ylabel('Minimum class accuracy (\\%)', fontsize=60)\n",
    "\n",
    "legend_elements = [\n",
    "    matplotlib.lines.Line2D([0], [0], c='k', linewidth=8, marker='o', markersize=20, label='ASA'),\n",
    "    matplotlib.lines.Line2D([0], [0], c='k', linewidth=8, linestyle='--', dashes=(7, 5), label='DANN'),\n",
    "    matplotlib.lines.Line2D([0], [0], c='k', linewidth=8, linestyle='dotted', label='No DA'),\n",
    "]\n",
    "\n",
    "plt.legend(handles=legend_elements, fontsize=60, ncol=3, \n",
    "           loc='lower center', bbox_to_anchor=(1.74, 0.95), frameon=False)\n",
    "\n",
    "\n",
    "plt.sca(axes[1])\n",
    "\n",
    "\n",
    "c_dist = hpalette[2]\n",
    "\n",
    "plt.yscale('log')\n",
    "plt.axhline(y=dann_dist_sq, c=c_dist, linewidth=8, linestyle='--', dashes=(7, 5))\n",
    "plt.axhline(y=noda_dist_sq, c=c_dist, linewidth=8, linestyle='dotted')\n",
    "plt.plot(x_points, dist_sq, c=c_dist, linewidth=8, marker='o', markersize=20)\n",
    "plt.xticks(x_points, h_points, fontsize=50)\n",
    "plt.yticks(fontsize=50)\n",
    "plt.margins(x=0.15)\n",
    "plt.grid(linestyle='--', dashes=(16, 16)) \n",
    "plt.xlabel('ASA history size', fontsize=60)\n",
    "plt.ylabel('Distribution distance', fontsize=60)\n",
    "\n",
    "\n",
    "plt.sca(axes[2])\n",
    "c_supp = hpalette[0]\n",
    "\n",
    "plt.yscale('symlog', linthreshy=0.05, linscaley=0.4)\n",
    "plt.axhline(y=dann_supp_sq, c=c_supp, linewidth=8, linestyle='--', dashes=(7, 5))\n",
    "plt.axhline(y=noda_supp_sq, c=c_supp, linewidth=8, linestyle='dotted')\n",
    "plt.plot(x_points, supp_sq, c=c_supp, linewidth=8, marker='o', markersize=20)\n",
    "\n",
    "\n",
    "\n",
    "plt.xticks(x_points, h_points, fontsize=50)\n",
    "\n",
    "tlabels = ['$10^{-2}$', '$5\\cdot 10^{-2}$', '$10^{-1}$', '$10^0$', '$10^1$', '$10^2$']\n",
    "plt.yticks([10**(-2.0), 5 * 10**(-2.0), 10**(-1.0), 10**(0.0), 10**(1.0), 10 ** (2.0)], tlabels, fontsize=50)\n",
    "plt.margins(x=0.15)\n",
    "plt.grid(linestyle='--', dashes=(16, 16)) \n",
    "plt.xlabel('ASA history size', fontsize=60)\n",
    "plt.ylabel('Support distance', fontsize=60)\n",
    "\n",
    "plt.subplots_adjust(wspace=0.34)\n",
    "plt.savefig(f'./figures/history_alignment.pdf', format='pdf', bbox_inches='tight')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Discriminator histogram plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "algorithms = [\n",
    "    'dann_zero',\n",
    "    'dann',    \n",
    "    'support_abs_h0',\n",
    "    'support_abs',    \n",
    "]\n",
    "\n",
    "\n",
    "regex_list = [f'0_usps_mnist/test_lenet_v3/seed_1/s_alpha_15/{algorithm_name}[^0_]' for algorithm_name in algorithms]\n",
    "runs = [list(query_runs(api, regex, step=65000)) for regex in regex_list]\n",
    "assert min([len(x) for x in runs]) == 1\n",
    "assert max([len(x) for x in runs]) == 1\n",
    "runs = [x[0] for x in runs]\n",
    "\n",
    "os.makedirs('./data_disc_hist', exist_ok=True)\n",
    "\n",
    "for run, algorithm_name in zip(runs, algorithms):\n",
    "    run.file('alignment_eval_outputs.pt').download('./data_disc_hist', replace=True)\n",
    "    shutil.move('./data_disc_hist/alignment_eval_outputs.pt', f'./data_disc_hist/{algorithm_name}.pt')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "def alignment_histplot(src_tensor, trg_tensor, title=None, legend=True, ylabel=True):\n",
    "\n",
    "    src_array = -src_tensor.numpy()\n",
    "    trg_array = -trg_tensor.numpy()\n",
    "\n",
    "    mx = max(src_array.max(), trg_array.max())\n",
    "    mn = min(src_array.min(), trg_array.min())\n",
    "    d = max(mx - mn, 0.01)\n",
    "    mn = mn - d * 0.12\n",
    "    mx = mx + d * 0.12\n",
    "    \n",
    "    ax = sns.kdeplot(x=src_array, color=palette[0] + (0.75,), linewidth=6, fill=False, label='source')\n",
    "    ax = sns.kdeplot(x=trg_array, color=palette[1] + (0.75,), linewidth=6, fill=False, label='target')\n",
    "      \n",
    "    l1 = ax.lines[0]\n",
    "    l2 = ax.lines[1]\n",
    "\n",
    "    # Get the xy data from the lines so that we can shade\n",
    "    x1 = l1.get_xydata()[:,0]\n",
    "    y1 = l1.get_xydata()[:,1]\n",
    "    x2 = l2.get_xydata()[:,0]\n",
    "    y2 = l2.get_xydata()[:,1]\n",
    "    ax.fill_between(x1,y1, color=palette[0], alpha=0.25)\n",
    "    ax.fill_between(x2,y2, color=palette[1], alpha=0.25)\n",
    "    \n",
    "    source_patch = matplotlib.patches.Patch(label='$g_\\\\sharp p$ (source)', edgecolor=palette[0] + (0.75,), \n",
    "                                            facecolor=palette[0] + (0.25,), linewidth=6)\n",
    "    target_patch = matplotlib.patches.Patch(label='$g_\\\\sharp q$ (target)', edgecolor=palette[1] + (0.75,), \n",
    "                                            facecolor=palette[1] + (0.25,), linewidth=6)    \n",
    "\n",
    "    if ylabel:\n",
    "        plt.ylabel('Density', fontsize=75, labelpad=20)\n",
    "    else:\n",
    "        plt.ylabel('', fontsize=75, labelpad=20)    \n",
    "    xlim = max(abs(mn), abs(mx))\n",
    "    plt.margins(y=0.25)    \n",
    "    if legend:        \n",
    "        plt.legend(handles=[source_patch, target_patch], fontsize=50, loc='upper left', bbox_to_anchor=(-0.025, 1.04), ncol=1, frameon=True, handlelength=1.25, columnspacing=1.0)\n",
    "\n",
    "    \n",
    "    \n",
    "    plt.gca().set_xlim(-xlim, xlim)\n",
    "    plt.grid(linestyle='--', dashes=(12, 12))    \n",
    "    ax = plt.gca()\n",
    "    \n",
    "    tick_labels = ax.xaxis.get_majorticklabels() + ax.yaxis.get_majorticklabels()        \n",
    "    for label in tick_labels:\n",
    "        label.set_fontsize(50)\n",
    "            \n",
    "    ax.tick_params(axis='both', which='major', pad=15)\n",
    "\n",
    "    if title is not None:\n",
    "        plt.title(title, fontsize=42, y=1.02)    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dann_zero\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "findfont: Font family ['serif'] not found. Falling back to DejaVu Sans.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 900x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dann\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 900x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "support_abs_h0\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 900x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "support_abs\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 900x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "algorithms = [\n",
    "    'dann_zero',\n",
    "    'dann',\n",
    "    'support_abs_h0',\n",
    "    'support_abs',    \n",
    "]\n",
    "\n",
    "\n",
    "for i, algorithm_name in enumerate(algorithms):\n",
    "    print(algorithm_name)\n",
    "    disc_outputs = torch.load(f'./data_disc_hist/{algorithm_name}.pt')['disc_outputs'][0]    \n",
    "    plt.figure(figsize=(12.5, 10.0))\n",
    "    alignment_histplot(*disc_outputs, title=None, legend= i == 0, ylabel=False)\n",
    "    plt.savefig(f'./figures/histplot_{algorithm_name}.pdf', format='pdf', bbox_inches='tight')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# STL->CIFAR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "stl_cifar/test_deep_cnn/seed_[1-5]/s_alpha_00/source_only[^0_]\n",
      "Runs: 5\n",
      "stl_cifar/test_deep_cnn/seed_[1-5]/s_alpha_10/source_only[^0_]\n",
      "Runs: 5\n",
      "stl_cifar/test_deep_cnn/seed_[1-5]/s_alpha_15/source_only[^0_]\n",
      "Runs: 5\n",
      "stl_cifar/test_deep_cnn/seed_[1-5]/s_alpha_20/source_only[^0_]\n",
      "Runs: 5\n",
      "\n",
      "stl_cifar/test_deep_cnn/seed_[1-5]/s_alpha_00/dann[^0_]\n",
      "Runs: 5\n",
      "stl_cifar/test_deep_cnn/seed_[1-5]/s_alpha_10/dann[^0_]\n",
      "Runs: 5\n",
      "stl_cifar/test_deep_cnn/seed_[1-5]/s_alpha_15/dann[^0_]\n",
      "Runs: 5\n",
      "stl_cifar/test_deep_cnn/seed_[1-5]/s_alpha_20/dann[^0_]\n",
      "Runs: 5\n",
      "\n",
      "stl_cifar/test_deep_cnn/seed_[1-5]/s_alpha_00/dann_ot[^0_]\n",
      "Runs: 5\n",
      "stl_cifar/test_deep_cnn/seed_[1-5]/s_alpha_10/dann_ot[^0_]\n",
      "Runs: 5\n",
      "stl_cifar/test_deep_cnn/seed_[1-5]/s_alpha_15/dann_ot[^0_]\n",
      "Runs: 5\n",
      "stl_cifar/test_deep_cnn/seed_[1-5]/s_alpha_20/dann_ot[^0_]\n",
      "Runs: 5\n",
      "\n",
      "stl_cifar/test_deep_cnn/seed_[1-5]/s_alpha_00/wgp_dann[^0_]\n",
      "Runs: 5\n",
      "stl_cifar/test_deep_cnn/seed_[1-5]/s_alpha_10/wgp_dann[^0_]\n",
      "Runs: 5\n",
      "stl_cifar/test_deep_cnn/seed_[1-5]/s_alpha_15/wgp_dann[^0_]\n",
      "Runs: 5\n",
      "stl_cifar/test_deep_cnn/seed_[1-5]/s_alpha_20/wgp_dann[^0_]\n",
      "Runs: 5\n",
      "\n",
      "stl_cifar/test_deep_cnn/seed_[1-5]/s_alpha_00/vada[^0_]\n",
      "Runs: 5\n",
      "stl_cifar/test_deep_cnn/seed_[1-5]/s_alpha_10/vada[^0_]\n",
      "Runs: 5\n",
      "stl_cifar/test_deep_cnn/seed_[1-5]/s_alpha_15/vada[^0_]\n",
      "Runs: 5\n",
      "stl_cifar/test_deep_cnn/seed_[1-5]/s_alpha_20/vada[^0_]\n",
      "Runs: 5\n",
      "\n",
      "stl_cifar/test_deep_cnn/seed_[1-5]/s_alpha_00/iwdan[^0_]\n",
      "Runs: 5\n",
      "stl_cifar/test_deep_cnn/seed_[1-5]/s_alpha_10/iwdan[^0_]\n",
      "Runs: 5\n",
      "stl_cifar/test_deep_cnn/seed_[1-5]/s_alpha_15/iwdan[^0_]\n",
      "Runs: 5\n",
      "stl_cifar/test_deep_cnn/seed_[1-5]/s_alpha_20/iwdan[^0_]\n",
      "Runs: 5\n",
      "\n",
      "stl_cifar/test_deep_cnn/seed_[1-5]/s_alpha_00/iwcdan[^0_]\n",
      "Runs: 5\n",
      "stl_cifar/test_deep_cnn/seed_[1-5]/s_alpha_10/iwcdan[^0_]\n",
      "Runs: 5\n",
      "stl_cifar/test_deep_cnn/seed_[1-5]/s_alpha_15/iwcdan[^0_]\n",
      "Runs: 5\n",
      "stl_cifar/test_deep_cnn/seed_[1-5]/s_alpha_20/iwcdan[^0_]\n",
      "Runs: 5\n",
      "\n",
      "stl_cifar/test_deep_cnn/seed_[1-5]/s_alpha_00/sdann_4[^0_]\n",
      "Runs: 5\n",
      "stl_cifar/test_deep_cnn/seed_[1-5]/s_alpha_10/sdann_4[^0_]\n",
      "Runs: 5\n",
      "stl_cifar/test_deep_cnn/seed_[1-5]/s_alpha_15/sdann_4[^0_]\n",
      "Runs: 5\n",
      "stl_cifar/test_deep_cnn/seed_[1-5]/s_alpha_20/sdann_4[^0_]\n",
      "Runs: 5\n",
      "\n",
      "stl_cifar/test_deep_cnn/seed_[1-5]/s_alpha_00/support_sq[^0_]\n",
      "Runs: 5\n",
      "stl_cifar/test_deep_cnn/seed_[1-5]/s_alpha_10/support_sq[^0_]\n",
      "Runs: 5\n",
      "stl_cifar/test_deep_cnn/seed_[1-5]/s_alpha_15/support_sq[^0_]\n",
      "Runs: 5\n",
      "stl_cifar/test_deep_cnn/seed_[1-5]/s_alpha_20/support_sq[^0_]\n",
      "Runs: 5\n",
      "\n",
      "stl_cifar/test_deep_cnn/seed_[1-5]/s_alpha_00/support_abs[^0_]\n",
      "Runs: 5\n",
      "stl_cifar/test_deep_cnn/seed_[1-5]/s_alpha_10/support_abs[^0_]\n",
      "Runs: 5\n",
      "stl_cifar/test_deep_cnn/seed_[1-5]/s_alpha_15/support_abs[^0_]\n",
      "Runs: 5\n",
      "stl_cifar/test_deep_cnn/seed_[1-5]/s_alpha_20/support_abs[^0_]\n",
      "Runs: 5\n",
      "\n"
     ]
    }
   ],
   "source": [
    "prefix = 'stl_cifar/test_deep_cnn' # change to srl_cifar_deep_cnn\n",
    "imbalance_levels = ['00', '10', '15', '20']\n",
    "algorithms = [\n",
    "    'source_only',\n",
    "    'dann',\n",
    "    'dann_ot',\n",
    "    'wgp_dann',\n",
    "    'vada',\n",
    "    'iwdan',\n",
    "    'iwcdan',    \n",
    "    'sdann_4',\n",
    "    'support_sq',\n",
    "    'support_abs',\n",
    "]\n",
    "\n",
    "results = ResultsTable(prefix, algorithms, imbalance_levels, num_steps=40000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "stl_cifar/test_deep_cnn eval/target_val/accuracy_class_avg\n",
      "| algorithm   | 00               | 10               | 15               | 20               |\n",
      "|:------------|:-----------------|:-----------------|:-----------------|:-----------------|\n",
      "| source_only | 69.73 (0.58) [5] | 68.62 (0.78) [5] | 66.78 (0.47) [5] | 65.45 (1.29) [5] |\n",
      "| dann        | 75.22 (0.41) [5] | 69.34 (1.02) [5] | 65.24 (1.61) [5] | 61.12 (4.03) [5] |\n",
      "| dann_ot     | 75.99 (0.50) [5] | 67.96 (1.13) [5] | 63.21 (2.35) [5] | 58.65 (4.78) [5] |\n",
      "| wgp_dann    | 74.99 (0.45) [5] | 66.72 (1.57) [5] | 60.97 (3.80) [5] | 57.42 (5.25) [5] |\n",
      "| vada        | 76.64 (0.15) [5] | 70.27 (1.09) [5] | 65.41 (2.15) [5] | 61.55 (4.15) [5] |\n",
      "| iwdan       | 70.39 (0.73) [5] | 68.67 (0.62) [5] | 66.71 (0.69) [5] | 64.40 (1.11) [5] |\n",
      "| iwcdan      | 70.21 (0.50) [5] | 69.04 (0.94) [5] | 66.06 (1.10) [5] | 64.48 (1.38) [5] |\n",
      "| sdann_4     | 71.66 (0.59) [5] | 71.04 (0.63) [5] | 69.24 (0.82) [5] | 66.78 (1.04) [5] |\n",
      "| support_sq  | 71.73 (0.22) [5] | 70.76 (0.58) [5] | 69.41 (0.40) [5] | 67.80 (0.61) [5] |\n",
      "| support_abs | 71.40 (0.42) [5] | 70.81 (0.36) [5] | 69.55 (0.59) [5] | 67.62 (0.99) [5] |\n"
     ]
    }
   ],
   "source": [
    "results.print_table(['eval/target_val/accuracy_class_avg'], ResultsTable.mean_std_fn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "stl_cifar/test_deep_cnn eval/target_val/accuracy_class_avg\n",
      "| algorithm   | 00                    | 10                    | 15                    | 20                    |\n",
      "|:------------|:----------------------|:----------------------|:----------------------|:----------------------|\n",
      "| source_only | 69.75 69.88 70.04 [5] | 68.30 68.80 69.25 [5] | 66.39 66.82 67.16 [5] | 64.76 65.84 66.68 [5] |\n",
      "| dann        | 74.87 75.26 75.38 [5] | 68.60 69.87 70.05 [5] | 63.74 64.93 67.07 [5] | 57.39 63.34 64.78 [5] |\n",
      "| dann_ot     | 75.85 75.97 76.04 [5] | 67.14 67.66 68.85 [5] | 60.93 64.50 65.12 [5] | 54.42 61.26 62.03 [5] |\n",
      "| wgp_dann    | 74.68 74.81 75.05 [5] | 65.33 67.73 67.92 [5] | 57.09 63.32 63.40 [5] | 54.26 59.03 61.85 [5] |\n",
      "| vada        | 76.55 76.66 76.72 [5] | 70.02 70.60 71.04 [5] | 65.42 66.07 66.48 [5] | 60.23 63.22 64.72 [5] |\n",
      "| iwdan       | 69.87 69.93 70.67 [5] | 68.64 68.72 69.14 [5] | 65.87 67.09 67.32 [5] | 63.56 64.39 64.86 [5] |\n",
      "| iwcdan      | 70.06 70.15 70.15 [5] | 69.11 69.36 69.36 [5] | 65.05 66.14 67.18 [5] | 63.85 64.49 65.15 [5] |\n",
      "| sdann_4     | 71.71 71.79 72.11 [5] | 70.42 71.13 71.68 [5] | 68.67 69.40 70.02 [5] | 66.17 66.36 67.92 [5] |\n",
      "| support_sq  | 71.66 71.69 71.93 [5] | 70.43 70.72 70.99 [5] | 69.20 69.22 69.31 [5] | 67.15 68.07 68.18 [5] |\n",
      "| support_abs | 71.20 71.58 71.72 [5] | 70.80 70.91 71.03 [5] | 69.63 69.64 69.85 [5] | 66.57 67.80 68.20 [5] |\n"
     ]
    }
   ],
   "source": [
    "results.print_table(['eval/target_val/accuracy_class_avg'], ResultsTable.quant_fn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "stl_cifar/test_deep_cnn eval/target_val/accuracy_class_min\n",
      "| algorithm   | 00                    | 10                    | 15                    | 20                    |\n",
      "|:------------|:----------------------|:----------------------|:----------------------|:----------------------|\n",
      "| source_only | 45.30 49.79 50.65 [5] | 45.35 47.21 48.15 [5] | 45.76 46.04 47.02 [5] | 41.58 43.74 44.65 [5] |\n",
      "| dann        | 54.25 54.61 56.63 [5] | 40.69 44.75 45.05 [5] | 33.95 34.87 36.83 [5] | 21.19 27.04 28.47 [5] |\n",
      "| dann_ot     | 54.32 55.25 55.52 [5] | 36.53 42.96 43.74 [5] | 29.28 34.42 34.58 [5] | 23.15 24.32 25.47 [5] |\n",
      "| wgp_dann    | 53.29 53.47 54.36 [5] | 34.36 38.62 40.95 [5] | 26.26 27.00 32.41 [5] | 18.62 21.89 22.46 [5] |\n",
      "| vada        | 53.50 56.89 58.32 [5] | 43.96 47.67 48.77 [5] | 33.27 35.74 39.31 [5] | 25.19 25.54 27.98 [5] |\n",
      "| iwdan       | 47.94 50.55 50.63 [5] | 44.75 45.84 50.52 [5] | 40.45 44.67 44.75 [5] | 34.47 36.77 37.88 [5] |\n",
      "| iwcdan      | 42.39 47.83 49.25 [5] | 46.34 47.12 51.32 [5] | 37.72 39.86 40.83 [5] | 35.52 36.96 40.22 [5] |\n",
      "| sdann_4     | 52.06 52.15 52.76 [5] | 48.12 49.85 51.84 [5] | 43.45 48.55 49.04 [5] | 33.56 39.01 47.05 [5] |\n",
      "| support_sq  | 46.65 52.87 53.44 [5] | 46.75 51.65 52.75 [5] | 43.27 45.61 52.04 [5] | 39.76 44.65 45.85 [5] |\n",
      "| support_abs | 48.40 48.95 53.47 [5] | 47.33 49.18 50.05 [5] | 42.08 43.19 49.45 [5] | 35.42 40.89 48.95 [5] |\n"
     ]
    }
   ],
   "source": [
    "results.print_table(['eval/target_val/accuracy_class_min'], ResultsTable.quant_fn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "stl_cifar/test_deep_cnn eval/target_val/accuracy_class_avg eval/target_val/accuracy_class_min\n",
      "\\begin{tabular}{lllll}\n",
      "\\hline\n",
      " algorithm   & 00                                                  & 10                                                  & 15                                                  & 20                                                  \\\\\n",
      "\\hline\n",
      " source_only & $ 69.9_{~69.8}^{~70.0} $ & $ 49.8_{~45.3}^{~50.6} $ & $ 68.8_{~68.3}^{~69.3} $ & $ 47.2_{~45.3}^{~48.2} $ & $ 66.8_{~66.4}^{~67.2} $ & $ 46.0_{~45.8}^{~47.0} $ & $ 65.8_{~64.8}^{~66.7} $ & $ 43.7_{~41.6}^{~44.6} $ \\\\\n",
      " dann        & $ 75.3_{~74.9}^{~75.4} $ & $ 54.6_{~54.2}^{~56.6} $ & $ 69.9_{~68.6}^{~70.1} $ & $ 44.8_{~40.7}^{~45.1} $ & $ 64.9_{~63.7}^{~67.1} $ & $ 34.9_{~33.9}^{~36.8} $ & $ 63.3_{~57.4}^{~64.8} $ & $ 27.0_{~21.2}^{~28.5} $ \\\\\n",
      " dann_ot     & $ 76.0_{~75.8}^{~76.0} $ & $ 55.2_{~54.3}^{~55.5} $ & $ 67.7_{~67.1}^{~68.9} $ & $ 43.0_{~36.5}^{~43.7} $ & $ 64.5_{~60.9}^{~65.1} $ & $ 34.4_{~29.3}^{~34.6} $ & $ 61.3_{~54.4}^{~62.0} $ & $ 24.3_{~23.2}^{~25.5} $ \\\\\n",
      " wgp_dann    & $ 74.8_{~74.7}^{~75.1} $ & $ 53.5_{~53.3}^{~54.4} $ & $ 67.7_{~65.3}^{~67.9} $ & $ 38.6_{~34.4}^{~41.0} $ & $ 63.3_{~57.1}^{~63.4} $ & $ 27.0_{~26.3}^{~32.4} $ & $ 59.0_{~54.3}^{~61.8} $ & $ 21.9_{~18.6}^{~22.5} $ \\\\\n",
      " vada        & $ 76.7_{~76.6}^{~76.7} $ & $ 56.9_{~53.5}^{~58.3} $ & $ 70.6_{~70.0}^{~71.0} $ & $ 47.7_{~44.0}^{~48.8} $ & $ 66.1_{~65.4}^{~66.5} $ & $ 35.7_{~33.3}^{~39.3} $ & $ 63.2_{~60.2}^{~64.7} $ & $ 25.5_{~25.2}^{~28.0} $ \\\\\n",
      " iwdan       & $ 69.9_{~69.9}^{~70.7} $ & $ 50.5_{~47.9}^{~50.6} $ & $ 68.7_{~68.6}^{~69.1} $ & $ 45.8_{~44.8}^{~50.5} $ & $ 67.1_{~65.9}^{~67.3} $ & $ 44.7_{~40.4}^{~44.8} $ & $ 64.4_{~63.6}^{~64.9} $ & $ 36.8_{~34.5}^{~37.9} $ \\\\\n",
      " iwcdan      & $ 70.1_{~70.1}^{~70.2} $ & $ 47.8_{~42.4}^{~49.3} $ & $ 69.4_{~69.1}^{~69.4} $ & $ 47.1_{~46.3}^{~51.3} $ & $ 66.1_{~65.0}^{~67.2} $ & $ 39.9_{~37.7}^{~40.8} $ & $ 64.5_{~63.9}^{~65.1} $ & $ 37.0_{~35.5}^{~40.2} $ \\\\\n",
      " sdann_4     & $ 71.8_{~71.7}^{~72.1} $ & $ 52.1_{~52.1}^{~52.8} $ & $ 71.1_{~70.4}^{~71.7} $ & $ 49.9_{~48.1}^{~51.8} $ & $ 69.4_{~68.7}^{~70.0} $ & $ 48.6_{~43.5}^{~49.0} $ & $ 66.4_{~66.2}^{~67.9} $ & $ 39.0_{~33.6}^{~47.1} $ \\\\\n",
      " support_sq  & $ 71.7_{~71.7}^{~71.9} $ & $ 52.9_{~46.7}^{~53.4} $ & $ 70.7_{~70.4}^{~71.0} $ & $ 51.6_{~46.8}^{~52.7} $ & $ 69.2_{~69.2}^{~69.3} $ & $ 45.6_{~43.3}^{~52.0} $ & $ 68.1_{~67.2}^{~68.2} $ & $ 44.7_{~39.8}^{~45.9} $ \\\\\n",
      " support_abs & $ 71.6_{~71.2}^{~71.7} $ & $ 49.0_{~48.4}^{~53.5} $ & $ 70.9_{~70.8}^{~71.0} $ & $ 49.2_{~47.3}^{~50.0} $ & $ 69.6_{~69.6}^{~69.9} $ & $ 43.2_{~42.1}^{~49.5} $ & $ 67.8_{~66.6}^{~68.2} $ & $ 40.9_{~35.4}^{~49.0} $ \\\\\n",
      "\\hline\n",
      "\\end{tabular}\n"
     ]
    }
   ],
   "source": [
    "results.print_table(['eval/target_val/accuracy_class_avg', 'eval/target_val/accuracy_class_min'], \n",
    "                    ResultsTable.quant_latex_fn, tablefmt='latex_raw', sep=' & ')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "stl_cifar/test_deep_cnn eval/target_val/accuracy_class_avg eval/target_val/accuracy_class_min\n",
      "| algorithm   | 00                                                | 10                                                | 15                                                | 20                                                |\n",
      "|:------------|:--------------------------------------------------|:--------------------------------------------------|:--------------------------------------------------|:--------------------------------------------------|\n",
      "| source_only | $ 69.9_{~69.8}^{~70.0} $ $ 49.8_{~45.3}^{~50.6} $ | $ 68.8_{~68.3}^{~69.3} $ $ 47.2_{~45.3}^{~48.2} $ | $ 66.8_{~66.4}^{~67.2} $ $ 46.0_{~45.8}^{~47.0} $ | $ 65.8_{~64.8}^{~66.7} $ $ 43.7_{~41.6}^{~44.6} $ |\n",
      "| dann        | $ 75.3_{~74.9}^{~75.4} $ $ 54.6_{~54.2}^{~56.6} $ | $ 69.9_{~68.6}^{~70.1} $ $ 44.8_{~40.7}^{~45.1} $ | $ 64.9_{~63.7}^{~67.1} $ $ 34.9_{~33.9}^{~36.8} $ | $ 63.3_{~57.4}^{~64.8} $ $ 27.0_{~21.2}^{~28.5} $ |\n",
      "| dann_ot     | $ 76.0_{~75.8}^{~76.0} $ $ 55.2_{~54.3}^{~55.5} $ | $ 67.7_{~67.1}^{~68.9} $ $ 43.0_{~36.5}^{~43.7} $ | $ 64.5_{~60.9}^{~65.1} $ $ 34.4_{~29.3}^{~34.6} $ | $ 61.3_{~54.4}^{~62.0} $ $ 24.3_{~23.2}^{~25.5} $ |\n",
      "| wgp_dann    | $ 74.8_{~74.7}^{~75.1} $ $ 53.5_{~53.3}^{~54.4} $ | $ 67.7_{~65.3}^{~67.9} $ $ 38.6_{~34.4}^{~41.0} $ | $ 63.3_{~57.1}^{~63.4} $ $ 27.0_{~26.3}^{~32.4} $ | $ 59.0_{~54.3}^{~61.8} $ $ 21.9_{~18.6}^{~22.5} $ |\n",
      "| vada        | $ 76.7_{~76.6}^{~76.7} $ $ 56.9_{~53.5}^{~58.3} $ | $ 70.6_{~70.0}^{~71.0} $ $ 47.7_{~44.0}^{~48.8} $ | $ 66.1_{~65.4}^{~66.5} $ $ 35.7_{~33.3}^{~39.3} $ | $ 63.2_{~60.2}^{~64.7} $ $ 25.5_{~25.2}^{~28.0} $ |\n",
      "| iwdan       | $ 69.9_{~69.9}^{~70.7} $ $ 50.5_{~47.9}^{~50.6} $ | $ 68.7_{~68.6}^{~69.1} $ $ 45.8_{~44.8}^{~50.5} $ | $ 67.1_{~65.9}^{~67.3} $ $ 44.7_{~40.4}^{~44.8} $ | $ 64.4_{~63.6}^{~64.9} $ $ 36.8_{~34.5}^{~37.9} $ |\n",
      "| iwcdan      | $ 70.1_{~70.1}^{~70.2} $ $ 47.8_{~42.4}^{~49.3} $ | $ 69.4_{~69.1}^{~69.4} $ $ 47.1_{~46.3}^{~51.3} $ | $ 66.1_{~65.0}^{~67.2} $ $ 39.9_{~37.7}^{~40.8} $ | $ 64.5_{~63.9}^{~65.1} $ $ 37.0_{~35.5}^{~40.2} $ |\n",
      "| sdann_4     | $ 71.8_{~71.7}^{~72.1} $ $ 52.1_{~52.1}^{~52.8} $ | $ 71.1_{~70.4}^{~71.7} $ $ 49.9_{~48.1}^{~51.8} $ | $ 69.4_{~68.7}^{~70.0} $ $ 48.6_{~43.5}^{~49.0} $ | $ 66.4_{~66.2}^{~67.9} $ $ 39.0_{~33.6}^{~47.1} $ |\n",
      "| support_sq  | $ 71.7_{~71.7}^{~71.9} $ $ 52.9_{~46.7}^{~53.4} $ | $ 70.7_{~70.4}^{~71.0} $ $ 51.6_{~46.8}^{~52.7} $ | $ 69.2_{~69.2}^{~69.3} $ $ 45.6_{~43.3}^{~52.0} $ | $ 68.1_{~67.2}^{~68.2} $ $ 44.7_{~39.8}^{~45.9} $ |\n",
      "| support_abs | $ 71.6_{~71.2}^{~71.7} $ $ 49.0_{~48.4}^{~53.5} $ | $ 70.9_{~70.8}^{~71.0} $ $ 49.2_{~47.3}^{~50.0} $ | $ 69.6_{~69.6}^{~69.9} $ $ 43.2_{~42.1}^{~49.5} $ | $ 67.8_{~66.6}^{~68.2} $ $ 40.9_{~35.4}^{~49.0} $ |\n"
     ]
    }
   ],
   "source": [
    "results.print_table(['eval/target_val/accuracy_class_avg', 'eval/target_val/accuracy_class_min'], ResultsTable.quant_latex_fn)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "| Algorithm   | $\\alpha = 0.0$                                    | $\\alpha = 1.0$                                | $\\alpha = 1.5$                                         | $\\alpha = 2.0$                                    |\n",
    "|:------------|:--------------------------------------------------|:--------------------------------------------------|:--------------------------------------------------|:--------------------------------------------------|\n",
    "| source_only | $ 69.9_{~69.8}^{~70.0} $ $ 49.8_{~45.3}^{~50.6} $ | $ 68.8_{~68.3}^{~69.3} $ $ 47.2_{~45.3}^{~48.2} $ | $ 66.8_{~66.4}^{~67.2} $ $ 46.0_{~45.8}^{~47.0} $ | $ 65.8_{~64.8}^{~66.7} $ $ 43.7_{~41.6}^{~44.6} $ |\n",
    "| dann        | $ 75.3_{~74.9}^{~75.4} $ $ 54.6_{~54.2}^{~56.6} $ | $ 69.9_{~68.6}^{~70.1} $ $ 44.8_{~40.7}^{~45.1} $ | $ 64.9_{~63.7}^{~67.1} $ $ 34.9_{~33.9}^{~36.8} $ | $ 63.3_{~57.4}^{~64.8} $ $ 27.0_{~21.2}^{~28.5} $ |\n",
    "| dann_ot     | $ 76.0_{~75.8}^{~76.0} $ $ 55.2_{~54.3}^{~55.5} $ | $ 67.7_{~67.1}^{~68.9} $ $ 43.0_{~36.5}^{~43.7} $ | $ 64.5_{~60.9}^{~65.1} $ $ 34.4_{~29.3}^{~34.6} $ | $ 61.3_{~54.4}^{~62.0} $ $ 24.3_{~23.2}^{~25.5} $ |\n",
    "| wgp_dann    | $ 74.8_{~74.7}^{~75.1} $ $ 53.5_{~53.3}^{~54.4} $ | $ 67.7_{~65.3}^{~67.9} $ $ 38.6_{~34.4}^{~41.0} $ | $ 63.3_{~57.1}^{~63.4} $ $ 27.0_{~26.3}^{~32.4} $ | $ 59.0_{~54.3}^{~61.8} $ $ 21.9_{~18.6}^{~22.5} $ |\n",
    "| vada        | $ 76.7_{~76.6}^{~76.7} $ $ 56.9_{~53.5}^{~58.3} $ | $ 70.6_{~70.0}^{~71.0} $ $ 47.7_{~44.0}^{~48.8} $ | $ 66.1_{~65.4}^{~66.5} $ $ 35.7_{~33.3}^{~39.3} $ | $ 63.2_{~60.2}^{~64.7} $ $ 25.5_{~25.2}^{~28.0} $ |\n",
    "| iwdan       | $ 69.9_{~69.9}^{~70.7} $ $ 50.5_{~47.9}^{~50.6} $ | $ 68.7_{~68.6}^{~69.1} $ $ 45.8_{~44.8}^{~50.5} $ | $ 67.1_{~65.9}^{~67.3} $ $ 44.7_{~40.4}^{~44.8} $ | $ 64.4_{~63.6}^{~64.9} $ $ 36.8_{~34.5}^{~37.9} $ |\n",
    "| iwcdan      | $ 70.1_{~70.1}^{~70.2} $ $ 47.8_{~42.4}^{~49.3} $ | $ 69.4_{~69.1}^{~69.4} $ $ 47.1_{~46.3}^{~51.3} $ | $ 66.1_{~65.0}^{~67.2} $ $ 39.9_{~37.7}^{~40.8} $ | $ 64.5_{~63.9}^{~65.1} $ $ 37.0_{~35.5}^{~40.2} $ |\n",
    "| sdann_4     | $ 71.8_{~71.7}^{~72.1} $ $ 52.1_{~52.1}^{~52.8} $ | $ 71.1_{~70.4}^{~71.7} $ $ 49.9_{~48.1}^{~51.8} $ | $ 69.4_{~68.7}^{~70.0} $ $ 48.6_{~43.5}^{~49.0} $ | $ 66.4_{~66.2}^{~67.9} $ $ 39.0_{~33.6}^{~47.1} $ |\n",
    "| support_sq  | $ 71.7_{~71.7}^{~71.9} $ $ 52.9_{~46.7}^{~53.4} $ | $ 70.7_{~70.4}^{~71.0} $ $ 51.6_{~46.8}^{~52.7} $ | $ 69.2_{~69.2}^{~69.3} $ $ 45.6_{~43.3}^{~52.0} $ | $ 68.1_{~67.2}^{~68.2} $ $ 44.7_{~39.8}^{~45.9} $ |\n",
    "| support_abs | $ 71.6_{~71.2}^{~71.7} $ $ 49.0_{~48.4}^{~53.5} $ | $ 70.9_{~70.8}^{~71.0} $ $ 49.2_{~47.3}^{~50.0} $ | $ 69.6_{~69.6}^{~69.9} $ $ 43.2_{~42.1}^{~49.5} $ | $ 67.8_{~66.6}^{~68.2} $ $ 40.9_{~35.4}^{~49.0} $ |"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "stl_cifar/test_deep_cnn eval/target_val/accuracy_class_avg eval/target_val/accuracy_class_min\n",
      "\\begin{tabular}{lllll}\n",
      "\\hline\n",
      " algorithm   & 00                                                  & 10                                                  & 15                                                  & 20                                                  \\\\\n",
      "\\hline\n",
      " source_only & $ 69.9_{~69.8}^{~70.0} $ & $ 49.8_{~45.3}^{~50.6} $ & $ 68.8_{~68.3}^{~69.3} $ & $ 47.2_{~45.3}^{~48.2} $ & $ 66.8_{~66.4}^{~67.2} $ & $ 46.0_{~45.8}^{~47.0} $ & $ 65.8_{~64.8}^{~66.7} $ & $ 43.7_{~41.6}^{~44.6} $ \\\\\n",
      " dann        & $ 75.3_{~74.9}^{~75.4} $ & $ 54.6_{~54.2}^{~56.6} $ & $ 69.9_{~68.6}^{~70.1} $ & $ 44.8_{~40.7}^{~45.1} $ & $ 64.9_{~63.7}^{~67.1} $ & $ 34.9_{~33.9}^{~36.8} $ & $ 63.3_{~57.4}^{~64.8} $ & $ 27.0_{~21.2}^{~28.5} $ \\\\\n",
      " dann_ot     & $ 76.0_{~75.8}^{~76.0} $ & $ 55.2_{~54.3}^{~55.5} $ & $ 67.7_{~67.1}^{~68.9} $ & $ 43.0_{~36.5}^{~43.7} $ & $ 64.5_{~60.9}^{~65.1} $ & $ 34.4_{~29.3}^{~34.6} $ & $ 61.3_{~54.4}^{~62.0} $ & $ 24.3_{~23.2}^{~25.5} $ \\\\\n",
      " wgp_dann    & $ 74.8_{~74.7}^{~75.1} $ & $ 53.5_{~53.3}^{~54.4} $ & $ 67.7_{~65.3}^{~67.9} $ & $ 38.6_{~34.4}^{~41.0} $ & $ 63.3_{~57.1}^{~63.4} $ & $ 27.0_{~26.3}^{~32.4} $ & $ 59.0_{~54.3}^{~61.8} $ & $ 21.9_{~18.6}^{~22.5} $ \\\\\n",
      " vada        & $ 76.7_{~76.6}^{~76.7} $ & $ 56.9_{~53.5}^{~58.3} $ & $ 70.6_{~70.0}^{~71.0} $ & $ 47.7_{~44.0}^{~48.8} $ & $ 66.1_{~65.4}^{~66.5} $ & $ 35.7_{~33.3}^{~39.3} $ & $ 63.2_{~60.2}^{~64.7} $ & $ 25.5_{~25.2}^{~28.0} $ \\\\\n",
      " iwdan       & $ 69.9_{~69.9}^{~70.7} $ & $ 50.5_{~47.9}^{~50.6} $ & $ 68.7_{~68.6}^{~69.1} $ & $ 45.8_{~44.8}^{~50.5} $ & $ 67.1_{~65.9}^{~67.3} $ & $ 44.7_{~40.4}^{~44.8} $ & $ 64.4_{~63.6}^{~64.9} $ & $ 36.8_{~34.5}^{~37.9} $ \\\\\n",
      " iwcdan      & $ 70.1_{~70.1}^{~70.2} $ & $ 47.8_{~42.4}^{~49.3} $ & $ 69.4_{~69.1}^{~69.4} $ & $ 47.1_{~46.3}^{~51.3} $ & $ 66.1_{~65.0}^{~67.2} $ & $ 39.9_{~37.7}^{~40.8} $ & $ 64.5_{~63.9}^{~65.1} $ & $ 37.0_{~35.5}^{~40.2} $ \\\\\n",
      " sdann_4     & $ 71.8_{~71.7}^{~72.1} $ & $ 52.1_{~52.1}^{~52.8} $ & $ 71.1_{~70.4}^{~71.7} $ & $ 49.9_{~48.1}^{~51.8} $ & $ 69.4_{~68.7}^{~70.0} $ & $ 48.6_{~43.5}^{~49.0} $ & $ 66.4_{~66.2}^{~67.9} $ & $ 39.0_{~33.6}^{~47.1} $ \\\\\n",
      " support_sq  & $ 71.7_{~71.7}^{~71.9} $ & $ 52.9_{~46.7}^{~53.4} $ & $ 70.7_{~70.4}^{~71.0} $ & $ 51.6_{~46.8}^{~52.7} $ & $ 69.2_{~69.2}^{~69.3} $ & $ 45.6_{~43.3}^{~52.0} $ & $ 68.1_{~67.2}^{~68.2} $ & $ 44.7_{~39.8}^{~45.9} $ \\\\\n",
      " support_abs & $ 71.6_{~71.2}^{~71.7} $ & $ 49.0_{~48.4}^{~53.5} $ & $ 70.9_{~70.8}^{~71.0} $ & $ 49.2_{~47.3}^{~50.0} $ & $ 69.6_{~69.6}^{~69.9} $ & $ 43.2_{~42.1}^{~49.5} $ & $ 67.8_{~66.6}^{~68.2} $ & $ 40.9_{~35.4}^{~49.0} $ \\\\\n",
      "\\hline\n",
      "\\end{tabular}\n"
     ]
    }
   ],
   "source": [
    "results.print_table(['eval/target_val/accuracy_class_avg', 'eval/target_val/accuracy_class_min'],\n",
    "                    ResultsTable.quant_latex_fn, tablefmt='latex_raw', sep=' & ')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## STL->CIFAR entropy experiment\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "stl_cifar/test_deep_cnn/seed_[1-5]/s_alpha_00/dann_no_trg_ent[^0_]\n",
      "Runs: 5\n",
      "stl_cifar/test_deep_cnn/seed_[1-5]/s_alpha_10/dann_no_trg_ent[^0_]\n",
      "Runs: 5\n",
      "stl_cifar/test_deep_cnn/seed_[1-5]/s_alpha_15/dann_no_trg_ent[^0_]\n",
      "Runs: 5\n",
      "stl_cifar/test_deep_cnn/seed_[1-5]/s_alpha_20/dann_no_trg_ent[^0_]\n",
      "Runs: 5\n",
      "\n",
      "stl_cifar/test_deep_cnn/seed_[1-5]/s_alpha_00/dann[^0_]\n",
      "Runs: 5\n",
      "stl_cifar/test_deep_cnn/seed_[1-5]/s_alpha_10/dann[^0_]\n",
      "Runs: 5\n",
      "stl_cifar/test_deep_cnn/seed_[1-5]/s_alpha_15/dann[^0_]\n",
      "Runs: 5\n",
      "stl_cifar/test_deep_cnn/seed_[1-5]/s_alpha_20/dann[^0_]\n",
      "Runs: 5\n",
      "\n",
      "stl_cifar/test_deep_cnn/seed_[1-5]/s_alpha_00/vada[^0_]\n",
      "Runs: 5\n",
      "stl_cifar/test_deep_cnn/seed_[1-5]/s_alpha_10/vada[^0_]\n",
      "Runs: 5\n",
      "stl_cifar/test_deep_cnn/seed_[1-5]/s_alpha_15/vada[^0_]\n",
      "Runs: 5\n",
      "stl_cifar/test_deep_cnn/seed_[1-5]/s_alpha_20/vada[^0_]\n",
      "Runs: 5\n",
      "\n",
      "stl_cifar/test_deep_cnn/seed_[1-5]/s_alpha_00/iwdan_no_trg_ent[^0_]\n",
      "Runs: 5\n",
      "stl_cifar/test_deep_cnn/seed_[1-5]/s_alpha_10/iwdan_no_trg_ent[^0_]\n",
      "Runs: 5\n",
      "stl_cifar/test_deep_cnn/seed_[1-5]/s_alpha_15/iwdan_no_trg_ent[^0_]\n",
      "Runs: 5\n",
      "stl_cifar/test_deep_cnn/seed_[1-5]/s_alpha_20/iwdan_no_trg_ent[^0_]\n",
      "Runs: 5\n",
      "\n",
      "stl_cifar/test_deep_cnn/seed_[1-5]/s_alpha_00/iwdan[^0_]\n",
      "Runs: 5\n",
      "stl_cifar/test_deep_cnn/seed_[1-5]/s_alpha_10/iwdan[^0_]\n",
      "Runs: 5\n",
      "stl_cifar/test_deep_cnn/seed_[1-5]/s_alpha_15/iwdan[^0_]\n",
      "Runs: 5\n",
      "stl_cifar/test_deep_cnn/seed_[1-5]/s_alpha_20/iwdan[^0_]\n",
      "Runs: 5\n",
      "\n",
      "stl_cifar/test_deep_cnn/seed_[1-5]/s_alpha_00/iwcdan_no_trg_ent[^0_]\n",
      "Runs: 5\n",
      "stl_cifar/test_deep_cnn/seed_[1-5]/s_alpha_10/iwcdan_no_trg_ent[^0_]\n",
      "Runs: 5\n",
      "stl_cifar/test_deep_cnn/seed_[1-5]/s_alpha_15/iwcdan_no_trg_ent[^0_]\n",
      "Runs: 5\n",
      "stl_cifar/test_deep_cnn/seed_[1-5]/s_alpha_20/iwcdan_no_trg_ent[^0_]\n",
      "Runs: 5\n",
      "\n",
      "stl_cifar/test_deep_cnn/seed_[1-5]/s_alpha_00/iwcdan[^0_]\n",
      "Runs: 5\n",
      "stl_cifar/test_deep_cnn/seed_[1-5]/s_alpha_10/iwcdan[^0_]\n",
      "Runs: 5\n",
      "stl_cifar/test_deep_cnn/seed_[1-5]/s_alpha_15/iwcdan[^0_]\n",
      "Runs: 5\n",
      "stl_cifar/test_deep_cnn/seed_[1-5]/s_alpha_20/iwcdan[^0_]\n",
      "Runs: 5\n",
      "\n",
      "stl_cifar/test_deep_cnn/seed_[1-5]/s_alpha_00/sdann_4_no_trg_ent[^0_]\n",
      "Runs: 5\n",
      "stl_cifar/test_deep_cnn/seed_[1-5]/s_alpha_10/sdann_4_no_trg_ent[^0_]\n",
      "Runs: 5\n",
      "stl_cifar/test_deep_cnn/seed_[1-5]/s_alpha_15/sdann_4_no_trg_ent[^0_]\n",
      "Runs: 5\n",
      "stl_cifar/test_deep_cnn/seed_[1-5]/s_alpha_20/sdann_4_no_trg_ent[^0_]\n",
      "Runs: 5\n",
      "\n",
      "stl_cifar/test_deep_cnn/seed_[1-5]/s_alpha_00/sdann_4[^0_]\n",
      "Runs: 5\n",
      "stl_cifar/test_deep_cnn/seed_[1-5]/s_alpha_10/sdann_4[^0_]\n",
      "Runs: 5\n",
      "stl_cifar/test_deep_cnn/seed_[1-5]/s_alpha_15/sdann_4[^0_]\n",
      "Runs: 5\n",
      "stl_cifar/test_deep_cnn/seed_[1-5]/s_alpha_20/sdann_4[^0_]\n",
      "Runs: 5\n",
      "\n",
      "stl_cifar/test_deep_cnn/seed_[1-5]/s_alpha_00/support_sq_no_trg_ent[^0_]\n",
      "Runs: 5\n",
      "stl_cifar/test_deep_cnn/seed_[1-5]/s_alpha_10/support_sq_no_trg_ent[^0_]\n",
      "Runs: 5\n",
      "stl_cifar/test_deep_cnn/seed_[1-5]/s_alpha_15/support_sq_no_trg_ent[^0_]\n",
      "Runs: 5\n",
      "stl_cifar/test_deep_cnn/seed_[1-5]/s_alpha_20/support_sq_no_trg_ent[^0_]\n",
      "Runs: 5\n",
      "\n",
      "stl_cifar/test_deep_cnn/seed_[1-5]/s_alpha_00/support_sq[^0_]\n",
      "Runs: 5\n",
      "stl_cifar/test_deep_cnn/seed_[1-5]/s_alpha_10/support_sq[^0_]\n",
      "Runs: 5\n",
      "stl_cifar/test_deep_cnn/seed_[1-5]/s_alpha_15/support_sq[^0_]\n",
      "Runs: 5\n",
      "stl_cifar/test_deep_cnn/seed_[1-5]/s_alpha_20/support_sq[^0_]\n",
      "Runs: 5\n",
      "\n",
      "stl_cifar/test_deep_cnn/seed_[1-5]/s_alpha_00/support_abs_no_trg_ent[^0_]\n",
      "Runs: 5\n",
      "stl_cifar/test_deep_cnn/seed_[1-5]/s_alpha_10/support_abs_no_trg_ent[^0_]\n",
      "Runs: 5\n",
      "stl_cifar/test_deep_cnn/seed_[1-5]/s_alpha_15/support_abs_no_trg_ent[^0_]\n",
      "Runs: 5\n",
      "stl_cifar/test_deep_cnn/seed_[1-5]/s_alpha_20/support_abs_no_trg_ent[^0_]\n",
      "Runs: 5\n",
      "\n",
      "stl_cifar/test_deep_cnn/seed_[1-5]/s_alpha_00/support_abs[^0_]\n",
      "Runs: 5\n",
      "stl_cifar/test_deep_cnn/seed_[1-5]/s_alpha_10/support_abs[^0_]\n",
      "Runs: 5\n",
      "stl_cifar/test_deep_cnn/seed_[1-5]/s_alpha_15/support_abs[^0_]\n",
      "Runs: 5\n",
      "stl_cifar/test_deep_cnn/seed_[1-5]/s_alpha_20/support_abs[^0_]\n",
      "Runs: 5\n",
      "\n"
     ]
    }
   ],
   "source": [
    "prefix = 'stl_cifar/test_deep_cnn' # change to stl_cifar/deep_cnn\n",
    "imbalance_levels = ['00', '10', '15', '20']\n",
    "algorithms = [ \n",
    "    'dann_no_trg_ent',\n",
    "    'dann',\n",
    "    'vada',\n",
    "    'iwdan_no_trg_ent',\n",
    "    'iwdan',\n",
    "    'iwcdan_no_trg_ent',\n",
    "    'iwcdan',    \n",
    "    'sdann_4_no_trg_ent',    \n",
    "    'sdann_4',        \n",
    "    'support_sq_no_trg_ent',\n",
    "    'support_sq',    \n",
    "    'support_abs_no_trg_ent',\n",
    "    'support_abs',    \n",
    "]\n",
    "\n",
    "results = ResultsTable(prefix, algorithms, imbalance_levels, num_steps=40000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "stl_cifar/test_deep_cnn eval/target_val/accuracy_class_avg\n",
      "| algorithm              | 00                    | 10                    | 15                    | 20                    |\n",
      "|:-----------------------|:----------------------|:----------------------|:----------------------|:----------------------|\n",
      "| dann_no_trg_ent        | 74.10 74.61 75.05 [5] | 66.96 68.40 69.18 [5] | 62.81 65.68 65.87 [5] | 60.00 62.50 64.60 [5] |\n",
      "| dann                   | 74.87 75.26 75.38 [5] | 68.60 69.87 70.05 [5] | 63.74 64.93 67.07 [5] | 57.39 63.34 64.78 [5] |\n",
      "| vada                   | 76.55 76.66 76.72 [5] | 70.02 70.60 71.04 [5] | 65.42 66.07 66.48 [5] | 60.23 63.22 64.72 [5] |\n",
      "| iwdan_no_trg_ent       | 70.20 70.35 70.67 [5] | 68.39 68.58 68.84 [5] | 66.03 66.72 67.87 [5] | 62.92 63.94 66.08 [5] |\n",
      "| iwdan                  | 69.87 69.93 70.67 [5] | 68.64 68.72 69.14 [5] | 65.87 67.09 67.32 [5] | 63.56 64.39 64.86 [5] |\n",
      "| iwcdan_no_trg_ent      | 70.01 70.07 70.76 [5] | 68.23 68.58 69.37 [5] | 65.88 65.97 66.00 [5] | 62.34 63.76 64.10 [5] |\n",
      "| iwcdan                 | 70.06 70.15 70.15 [5] | 69.11 69.36 69.36 [5] | 65.05 66.14 67.18 [5] | 63.85 64.49 65.15 [5] |\n",
      "| sdann_4_no_trg_ent     | 68.85 69.38 70.04 [5] | 69.32 69.58 69.72 [5] | 67.77 67.95 68.60 [5] | 64.19 66.33 66.42 [5] |\n",
      "| sdann_4                | 71.71 71.79 72.11 [5] | 70.42 71.13 71.68 [5] | 68.67 69.40 70.02 [5] | 66.17 66.36 67.92 [5] |\n",
      "| support_sq_no_trg_ent  | 69.86 69.92 70.28 [5] | 68.59 68.79 68.94 [5] | 67.21 68.07 68.72 [5] | 65.59 65.73 66.40 [5] |\n",
      "| support_sq             | 71.66 71.69 71.93 [5] | 70.43 70.72 70.99 [5] | 69.20 69.22 69.31 [5] | 67.15 68.07 68.18 [5] |\n",
      "| support_abs_no_trg_ent | 68.93 69.78 69.98 [5] | 68.36 68.40 68.62 [5] | 67.01 67.92 68.08 [5] | 65.70 66.27 66.90 [5] |\n",
      "| support_abs            | 71.20 71.58 71.72 [5] | 70.80 70.91 71.03 [5] | 69.63 69.64 69.85 [5] | 66.57 67.80 68.20 [5] |\n"
     ]
    }
   ],
   "source": [
    "results.print_table(['eval/target_val/accuracy_class_avg'], ResultsTable.quant_fn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "stl_cifar/test_deep_cnn eval/target_val/accuracy_class_min\n",
      "| algorithm              | 00                    | 10                    | 15                    | 20                    |\n",
      "|:-----------------------|:----------------------|:----------------------|:----------------------|:----------------------|\n",
      "| dann_no_trg_ent        | 49.90 51.52 55.04 [5] | 41.19 43.15 43.68 [5] | 29.57 35.47 36.15 [5] | 25.68 27.45 27.49 [5] |\n",
      "| dann                   | 54.25 54.61 56.63 [5] | 40.69 44.75 45.05 [5] | 33.95 34.87 36.83 [5] | 21.19 27.04 28.47 [5] |\n",
      "| vada                   | 53.50 56.89 58.32 [5] | 43.96 47.67 48.77 [5] | 33.27 35.74 39.31 [5] | 25.19 25.54 27.98 [5] |\n",
      "| iwdan_no_trg_ent       | 46.75 47.22 48.02 [5] | 43.16 43.62 46.34 [5] | 43.27 44.67 46.15 [5] | 32.69 36.48 37.34 [5] |\n",
      "| iwdan                  | 47.94 50.55 50.63 [5] | 44.75 45.84 50.52 [5] | 40.45 44.67 44.75 [5] | 34.47 36.77 37.88 [5] |\n",
      "| iwcdan_no_trg_ent      | 49.05 50.54 50.85 [5] | 41.16 44.23 45.76 [5] | 43.72 44.95 47.83 [5] | 33.64 37.33 37.66 [5] |\n",
      "| iwcdan                 | 42.39 47.83 49.25 [5] | 46.34 47.12 51.32 [5] | 37.72 39.86 40.83 [5] | 35.52 36.96 40.22 [5] |\n",
      "| sdann_4_no_trg_ent     | 45.10 46.45 49.69 [5] | 47.43 49.05 49.17 [5] | 42.57 48.25 48.75 [5] | 36.63 40.66 42.86 [5] |\n",
      "| sdann_4                | 52.06 52.15 52.76 [5] | 48.12 49.85 51.84 [5] | 43.45 48.55 49.04 [5] | 33.56 39.01 47.05 [5] |\n",
      "| support_sq_no_trg_ent  | 46.55 48.00 50.10 [5] | 45.26 47.25 49.31 [5] | 45.25 45.36 47.76 [5] | 41.34 43.56 45.02 [5] |\n",
      "| support_sq             | 46.65 52.87 53.44 [5] | 46.75 51.65 52.75 [5] | 43.27 45.61 52.04 [5] | 39.76 44.65 45.85 [5] |\n",
      "| support_abs_no_trg_ent | 45.37 45.65 48.02 [5] | 43.99 44.26 46.75 [5] | 40.40 46.63 48.44 [5] | 40.30 41.63 44.93 [5] |\n",
      "| support_abs            | 48.40 48.95 53.47 [5] | 47.33 49.18 50.05 [5] | 42.08 43.19 49.45 [5] | 35.42 40.89 48.95 [5] |\n"
     ]
    }
   ],
   "source": [
    "results.print_table(['eval/target_val/accuracy_class_min'], ResultsTable.quant_fn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "stl_cifar/test_deep_cnn eval/target_val/accuracy_class_avg eval/target_val/accuracy_class_min\n",
      "| algorithm              | 00                                                | 10                                                | 15                                                | 20                                                |\n",
      "|:-----------------------|:--------------------------------------------------|:--------------------------------------------------|:--------------------------------------------------|:--------------------------------------------------|\n",
      "| dann_no_trg_ent        | $ 74.6_{~74.1}^{~75.1} $ $ 51.5_{~49.9}^{~55.0} $ | $ 68.4_{~67.0}^{~69.2} $ $ 43.2_{~41.2}^{~43.7} $ | $ 65.7_{~62.8}^{~65.9} $ $ 35.5_{~29.6}^{~36.2} $ | $ 62.5_{~60.0}^{~64.6} $ $ 27.5_{~25.7}^{~27.5} $ |\n",
      "| dann                   | $ 75.3_{~74.9}^{~75.4} $ $ 54.6_{~54.2}^{~56.6} $ | $ 69.9_{~68.6}^{~70.1} $ $ 44.8_{~40.7}^{~45.1} $ | $ 64.9_{~63.7}^{~67.1} $ $ 34.9_{~33.9}^{~36.8} $ | $ 63.3_{~57.4}^{~64.8} $ $ 27.0_{~21.2}^{~28.5} $ |\n",
      "| vada                   | $ 76.7_{~76.6}^{~76.7} $ $ 56.9_{~53.5}^{~58.3} $ | $ 70.6_{~70.0}^{~71.0} $ $ 47.7_{~44.0}^{~48.8} $ | $ 66.1_{~65.4}^{~66.5} $ $ 35.7_{~33.3}^{~39.3} $ | $ 63.2_{~60.2}^{~64.7} $ $ 25.5_{~25.2}^{~28.0} $ |\n",
      "| iwdan_no_trg_ent       | $ 70.4_{~70.2}^{~70.7} $ $ 47.2_{~46.8}^{~48.0} $ | $ 68.6_{~68.4}^{~68.8} $ $ 43.6_{~43.2}^{~46.3} $ | $ 66.7_{~66.0}^{~67.9} $ $ 44.7_{~43.3}^{~46.2} $ | $ 63.9_{~62.9}^{~66.1} $ $ 36.5_{~32.7}^{~37.3} $ |\n",
      "| iwdan                  | $ 69.9_{~69.9}^{~70.7} $ $ 50.5_{~47.9}^{~50.6} $ | $ 68.7_{~68.6}^{~69.1} $ $ 45.8_{~44.8}^{~50.5} $ | $ 67.1_{~65.9}^{~67.3} $ $ 44.7_{~40.4}^{~44.8} $ | $ 64.4_{~63.6}^{~64.9} $ $ 36.8_{~34.5}^{~37.9} $ |\n",
      "| iwcdan_no_trg_ent      | $ 70.1_{~70.0}^{~70.8} $ $ 50.5_{~49.1}^{~50.8} $ | $ 68.6_{~68.2}^{~69.4} $ $ 44.2_{~41.2}^{~45.8} $ | $ 66.0_{~65.9}^{~66.0} $ $ 45.0_{~43.7}^{~47.8} $ | $ 63.8_{~62.3}^{~64.1} $ $ 37.3_{~33.6}^{~37.7} $ |\n",
      "| iwcdan                 | $ 70.1_{~70.1}^{~70.2} $ $ 47.8_{~42.4}^{~49.3} $ | $ 69.4_{~69.1}^{~69.4} $ $ 47.1_{~46.3}^{~51.3} $ | $ 66.1_{~65.0}^{~67.2} $ $ 39.9_{~37.7}^{~40.8} $ | $ 64.5_{~63.9}^{~65.1} $ $ 37.0_{~35.5}^{~40.2} $ |\n",
      "| sdann_4_no_trg_ent     | $ 69.4_{~68.8}^{~70.0} $ $ 46.5_{~45.1}^{~49.7} $ | $ 69.6_{~69.3}^{~69.7} $ $ 49.1_{~47.4}^{~49.2} $ | $ 68.0_{~67.8}^{~68.6} $ $ 48.2_{~42.6}^{~48.8} $ | $ 66.3_{~64.2}^{~66.4} $ $ 40.7_{~36.6}^{~42.9} $ |\n",
      "| sdann_4                | $ 71.8_{~71.7}^{~72.1} $ $ 52.1_{~52.1}^{~52.8} $ | $ 71.1_{~70.4}^{~71.7} $ $ 49.9_{~48.1}^{~51.8} $ | $ 69.4_{~68.7}^{~70.0} $ $ 48.6_{~43.5}^{~49.0} $ | $ 66.4_{~66.2}^{~67.9} $ $ 39.0_{~33.6}^{~47.1} $ |\n",
      "| support_sq_no_trg_ent  | $ 69.9_{~69.9}^{~70.3} $ $ 48.0_{~46.6}^{~50.1} $ | $ 68.8_{~68.6}^{~68.9} $ $ 47.3_{~45.3}^{~49.3} $ | $ 68.1_{~67.2}^{~68.7} $ $ 45.4_{~45.2}^{~47.8} $ | $ 65.7_{~65.6}^{~66.4} $ $ 43.6_{~41.3}^{~45.0} $ |\n",
      "| support_sq             | $ 71.7_{~71.7}^{~71.9} $ $ 52.9_{~46.7}^{~53.4} $ | $ 70.7_{~70.4}^{~71.0} $ $ 51.6_{~46.8}^{~52.7} $ | $ 69.2_{~69.2}^{~69.3} $ $ 45.6_{~43.3}^{~52.0} $ | $ 68.1_{~67.2}^{~68.2} $ $ 44.7_{~39.8}^{~45.9} $ |\n",
      "| support_abs_no_trg_ent | $ 69.8_{~68.9}^{~70.0} $ $ 45.7_{~45.4}^{~48.0} $ | $ 68.4_{~68.4}^{~68.6} $ $ 44.3_{~44.0}^{~46.8} $ | $ 67.9_{~67.0}^{~68.1} $ $ 46.6_{~40.4}^{~48.4} $ | $ 66.3_{~65.7}^{~66.9} $ $ 41.6_{~40.3}^{~44.9} $ |\n",
      "| support_abs            | $ 71.6_{~71.2}^{~71.7} $ $ 49.0_{~48.4}^{~53.5} $ | $ 70.9_{~70.8}^{~71.0} $ $ 49.2_{~47.3}^{~50.0} $ | $ 69.6_{~69.6}^{~69.9} $ $ 43.2_{~42.1}^{~49.5} $ | $ 67.8_{~66.6}^{~68.2} $ $ 40.9_{~35.4}^{~49.0} $ |\n"
     ]
    }
   ],
   "source": [
    "results.print_table(['eval/target_val/accuracy_class_avg', 'eval/target_val/accuracy_class_min'], ResultsTable.quant_latex_fn)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "| Algorithm   | $\\lambda_{\\text{ent}}$ | $\\alpha = 0.0$                                    | $\\alpha = 1.0$                                | $\\alpha = 1.5$                                         | $\\alpha = 2.0$                                    |\n",
    "|:------------|:-----------------------|:--------------------------------------------------|:--------------------------------------------------|:--------------------------------------------------|:--------------------------------------------------|\n",
    "| DANN        | $0$                    |$ 74.6_{74.1}^{75.1} $ $ 51.5_{49.9}^{55.0} $ | $ 68.4_{67.0}^{69.2} $ $ 43.2_{41.2}^{43.7} $ | $ 65.7_{62.8}^{65.9} $ $ 35.5_{29.6}^{36.2} $ | $ 62.5_{60.0}^{64.6} $ $ 27.5_{25.7}^{27.5} $ |\n",
    "| DANN        | $0.1$                  | $ 75.3_{74.9}^{75.4} $ $ 54.6_{54.2}^{56.6} $ | $ 69.9_{68.6}^{70.1} $ $ 44.8_{40.7}^{45.1} $ | $ 64.9_{63.7}^{67.1} $ $ 34.9_{33.9}^{36.8} $ | $ 63.3_{57.4}^{64.8} $ $ 27.0_{21.2}^{28.5} $ |\n",
    "| VADA        | $0.1$                  | $ 76.7_{76.6}^{76.7} $ $ 56.9_{53.5}^{58.3} $ | $ 70.6_{70.0}^{71.0} $ $ 47.7_{44.0}^{48.8} $ | $ 66.1_{65.4}^{66.5} $ $ 35.7_{33.3}^{39.3} $ | $ 63.2_{60.2}^{64.7} $ $ 25.5_{25.2}^{28.0} $ |\n",
    "| IWDAN       | $0$                    | $ 70.4_{70.2}^{70.7} $ $ 47.2_{46.8}^{48.0} $ | $ 68.6_{68.4}^{68.8} $ $ 43.6_{43.2}^{46.3} $ | $ 66.7_{66.0}^{67.9} $ $ 44.7_{43.3}^{46.2} $ | $ 63.9_{62.9}^{66.1} $ $ 36.5_{32.7}^{37.3} $ |\n",
    "| IWDAN       | $0.1$                  | $ 69.9_{69.9}^{70.7} $ $ 50.5_{47.9}^{50.6} $ | $ 68.7_{68.6}^{69.1} $ $ 45.8_{44.8}^{50.5} $ | $ 67.1_{65.9}^{67.3} $ $ 44.7_{40.4}^{44.8} $ | $ 64.4_{63.6}^{64.9} $ $ 36.8_{34.5}^{37.9} $ |\n",
    "| IWCDAN      | $0$                    | $ 70.1_{70.0}^{70.8} $ $ 50.5_{49.1}^{50.8} $ | $ 68.6_{68.2}^{69.4} $ $ 44.2_{41.2}^{45.8} $ | $ 66.0_{65.9}^{66.0} $ $ 45.0_{43.7}^{47.8} $ | $ 63.8_{62.3}^{64.1} $ $ 37.3_{33.6}^{37.7} $ |\n",
    "| IWCDAN      | $0.1$                  | $ 70.1_{70.1}^{70.2} $ $ 47.8_{42.4}^{49.3} $ | $ 69.4_{69.1}^{69.4} $ $ 47.1_{46.3}^{51.3} $ | $ 66.1_{65.0}^{67.2} $ $ 39.9_{37.7}^{40.8} $ | $ 64.5_{63.9}^{65.1} $ $ 37.0_{35.5}^{40.2} $ |\n",
    "| sDANN-4     | $0$                  | $ 69.4_{68.8}^{70.0} $ $ 46.5_{45.1}^{49.7} $ | $ 69.6_{69.3}^{69.7} $ $ 49.1_{47.4}^{49.2} $ | $ 68.0_{67.8}^{68.6} $ $ 48.2_{42.6}^{48.8} $ | $ 66.3_{64.2}^{66.4} $ $ 40.7_{36.6}^{42.9} $ |\n",
    "| sDANN-4     | $0.1$                | $ 71.8_{71.7}^{72.1} $ $ 52.1_{52.1}^{52.8} $ | $ 71.1_{70.4}^{71.7} $ $ 49.9_{48.1}^{51.8} $ | $ 69.4_{68.7}^{70.0} $ $ 48.6_{43.5}^{49.0} $ | $ 66.4_{66.2}^{67.9} $ $ 39.0_{33.6}^{47.1} $ |\n",
    "| ASA-sq      | $0$                  | $ 69.9_{69.9}^{70.3} $ $ 48.0_{46.6}^{50.1} $ | $ 68.8_{68.6}^{68.9} $ $ 47.3_{45.3}^{49.3} $ | $ 68.1_{67.2}^{68.7} $ $ 45.4_{45.2}^{47.8} $ | $ 65.7_{65.6}^{66.4} $ $ 43.6_{41.3}^{45.0} $ |\n",
    "| ASA-sq      | $0.1$                | $ 71.7_{71.7}^{71.9} $ $ 52.9_{46.7}^{53.4} $ | $ 70.7_{70.4}^{71.0} $ $ 51.6_{46.8}^{52.7} $ | $ 69.2_{69.2}^{69.3} $ $ 45.6_{43.3}^{52.0} $ | $ 68.1_{67.2}^{68.2} $ $ 44.7_{39.8}^{45.9} $ |\n",
    "| ASA-abs     | $0$                  | $ 69.8_{68.9}^{70.0} $ $ 45.7_{45.4}^{48.0} $ | $ 68.4_{68.4}^{68.6} $ $ 44.3_{44.0}^{46.8} $ | $ 67.9_{67.0}^{68.1} $ $ 46.6_{40.4}^{48.4} $ | $ 66.3_{65.7}^{66.9} $ $ 41.6_{40.3}^{44.9} $ |\n",
    "| ASA-abs     | $0.1$                | $ 71.6_{71.2}^{71.7} $ $ 49.0_{48.4}^{53.5} $ | $ 70.9_{70.8}^{71.0} $ $ 49.2_{47.3}^{50.0} $ | $ 69.6_{69.6}^{69.9} $ $ 43.2_{42.1}^{49.5} $ | $ 67.8_{66.6}^{68.2} $ $ 40.9_{35.4}^{49.0} $ |"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "| Algorithm   | $\\lambda_{\\text{ent}}$ | $\\alpha = 0.0$                                    | $\\alpha = 1.0$                                | $\\alpha = 1.5$                                         | $\\alpha = 2.0$                                    |\n",
    "|:------------|:-----------------------|:--------------------------------------------------|:--------------------------------------------------|:--------------------------------------------------|:--------------------------------------------------|\n",
    "| DANN        | $0$                    |$ 74.6_{74.1}^{75.1} $ $ 51.5_{49.9}^{55.0} $ | $ 68.4_{67.0}^{69.2} $ $ 43.2_{41.2}^{43.7} $ | $ 65.7_{62.8}^{65.9} $ $ 35.5_{29.6}^{36.2} $ | $ 62.5_{60.0}^{64.6} $ $ 27.5_{25.7}^{27.5} $ |\n",
    "| IWDAN       | $0$                    | $ 70.4_{70.2}^{70.7} $ $ 47.2_{46.8}^{48.0} $ | $ 68.6_{68.4}^{68.8} $ $ 43.6_{43.2}^{46.3} $ | $ 66.7_{66.0}^{67.9} $ $ 44.7_{43.3}^{46.2} $ | $ 63.9_{62.9}^{66.1} $ $ 36.5_{32.7}^{37.3} $ |\n",
    "| IWCDAN      | $0$                    | $ 70.1_{70.0}^{70.8} $ $ 50.5_{49.1}^{50.8} $ | $ 68.6_{68.2}^{69.4} $ $ 44.2_{41.2}^{45.8} $ | $ 66.0_{65.9}^{66.0} $ $ 45.0_{43.7}^{47.8} $ | $ 63.8_{62.3}^{64.1} $ $ 37.3_{33.6}^{37.7} $ |\n",
    "| sDANN-4     | $0$                  | $ 69.4_{68.8}^{70.0} $ $ 46.5_{45.1}^{49.7} $ | $ 69.6_{69.3}^{69.7} $ $ 49.1_{47.4}^{49.2} $ | $ 68.0_{67.8}^{68.6} $ $ 48.2_{42.6}^{48.8} $ | $ 66.3_{64.2}^{66.4} $ $ 40.7_{36.6}^{42.9} $ |\n",
    "| ASA-sq      | $0$                  | $ 69.9_{69.9}^{70.3} $ $ 48.0_{46.6}^{50.1} $ | $ 68.8_{68.6}^{68.9} $ $ 47.3_{45.3}^{49.3} $ | $ 68.1_{67.2}^{68.7} $ $ 45.4_{45.2}^{47.8} $ | $ 65.7_{65.6}^{66.4} $ $ 43.6_{41.3}^{45.0} $ |\n",
    "| ASA-abs     | $0$                  | $ 69.8_{68.9}^{70.0} $ $ 45.7_{45.4}^{48.0} $ | $ 68.4_{68.4}^{68.6} $ $ 44.3_{44.0}^{46.8} $ | $ 67.9_{67.0}^{68.1} $ $ 46.6_{40.4}^{48.4} $ | $ 66.3_{65.7}^{66.9} $ $ 41.6_{40.3}^{44.9} $ |"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "| Algorithm   | $\\lambda_{\\text{ent}}$ | $\\alpha = 0.0$                                    | $\\alpha = 1.0$                                | $\\alpha = 1.5$                                         | $\\alpha = 2.0$                                    |\n",
    "|:------------|:-----------------------|:--------------------------------------------------|:--------------------------------------------------|:--------------------------------------------------|:--------------------------------------------------|\n",
    "| DANN        | $0.1$                  | $ 75.3_{74.9}^{75.4} $ $ 54.6_{54.2}^{56.6} $ | $ 69.9_{68.6}^{70.1} $ $ 44.8_{40.7}^{45.1} $ | $ 64.9_{63.7}^{67.1} $ $ 34.9_{33.9}^{36.8} $ | $ 63.3_{57.4}^{64.8} $ $ 27.0_{21.2}^{28.5} $ |\n",
    "| IWDAN       | $0.1$                  | $ 69.9_{69.9}^{70.7} $ $ 50.5_{47.9}^{50.6} $ | $ 68.7_{68.6}^{69.1} $ $ 45.8_{44.8}^{50.5} $ | $ 67.1_{65.9}^{67.3} $ $ 44.7_{40.4}^{44.8} $ | $ 64.4_{63.6}^{64.9} $ $ 36.8_{34.5}^{37.9} $ |\n",
    "| IWCDAN      | $0.1$                  | $ 70.1_{70.1}^{70.2} $ $ 47.8_{42.4}^{49.3} $ | $ 69.4_{69.1}^{69.4} $ $ 47.1_{46.3}^{51.3} $ | $ 66.1_{65.0}^{67.2} $ $ 39.9_{37.7}^{40.8} $ | $ 64.5_{63.9}^{65.1} $ $ 37.0_{35.5}^{40.2} $ |\n",
    "| sDANN-4     | $0.1$                | $ 71.8_{71.7}^{72.1} $ $ 52.1_{52.1}^{52.8} $ | $ 71.1_{70.4}^{71.7} $ $ 49.9_{48.1}^{51.8} $ | $ 69.4_{68.7}^{70.0} $ $ 48.6_{43.5}^{49.0} $ | $ 66.4_{66.2}^{67.9} $ $ 39.0_{33.6}^{47.1} $ |\n",
    "| ASA-sq      | $0.1$                | $ 71.7_{71.7}^{71.9} $ $ 52.9_{46.7}^{53.4} $ | $ 70.7_{70.4}^{71.0} $ $ 51.6_{46.8}^{52.7} $ | $ 69.2_{69.2}^{69.3} $ $ 45.6_{43.3}^{52.0} $ | $ 68.1_{67.2}^{68.2} $ $ 44.7_{39.8}^{45.9} $ |\n",
    "| ASA-abs     | $0.1$                | $ 71.6_{71.2}^{71.7} $ $ 49.0_{48.4}^{53.5} $ | $ 70.9_{70.8}^{71.0} $ $ 49.2_{47.3}^{50.0} $ | $ 69.6_{69.6}^{69.9} $ $ 43.2_{42.1}^{49.5} $ | $ 67.8_{66.6}^{68.2} $ $ 40.9_{35.4}^{49.0} $ |"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "stl_cifar/test_deep_cnn eval/target_val/accuracy_class_avg eval/target_val/accuracy_class_min\n",
      "\\begin{tabular}{lllll}\n",
      "\\hline\n",
      " algorithm              & 00                                                  & 10                                                  & 15                                                  & 20                                                  \\\\\n",
      "\\hline\n",
      " dann_no_trg_ent        & $ 74.6_{~74.1}^{~75.1} $ & $ 51.5_{~49.9}^{~55.0} $ & $ 68.4_{~67.0}^{~69.2} $ & $ 43.2_{~41.2}^{~43.7} $ & $ 65.7_{~62.8}^{~65.9} $ & $ 35.5_{~29.6}^{~36.2} $ & $ 62.5_{~60.0}^{~64.6} $ & $ 27.5_{~25.7}^{~27.5} $ \\\\\n",
      " dann                   & $ 75.3_{~74.9}^{~75.4} $ & $ 54.6_{~54.2}^{~56.6} $ & $ 69.9_{~68.6}^{~70.1} $ & $ 44.8_{~40.7}^{~45.1} $ & $ 64.9_{~63.7}^{~67.1} $ & $ 34.9_{~33.9}^{~36.8} $ & $ 63.3_{~57.4}^{~64.8} $ & $ 27.0_{~21.2}^{~28.5} $ \\\\\n",
      " vada                   & $ 76.7_{~76.6}^{~76.7} $ & $ 56.9_{~53.5}^{~58.3} $ & $ 70.6_{~70.0}^{~71.0} $ & $ 47.7_{~44.0}^{~48.8} $ & $ 66.1_{~65.4}^{~66.5} $ & $ 35.7_{~33.3}^{~39.3} $ & $ 63.2_{~60.2}^{~64.7} $ & $ 25.5_{~25.2}^{~28.0} $ \\\\\n",
      " iwdan_no_trg_ent       & $ 70.4_{~70.2}^{~70.7} $ & $ 47.2_{~46.8}^{~48.0} $ & $ 68.6_{~68.4}^{~68.8} $ & $ 43.6_{~43.2}^{~46.3} $ & $ 66.7_{~66.0}^{~67.9} $ & $ 44.7_{~43.3}^{~46.2} $ & $ 63.9_{~62.9}^{~66.1} $ & $ 36.5_{~32.7}^{~37.3} $ \\\\\n",
      " iwdan                  & $ 69.9_{~69.9}^{~70.7} $ & $ 50.5_{~47.9}^{~50.6} $ & $ 68.7_{~68.6}^{~69.1} $ & $ 45.8_{~44.8}^{~50.5} $ & $ 67.1_{~65.9}^{~67.3} $ & $ 44.7_{~40.4}^{~44.8} $ & $ 64.4_{~63.6}^{~64.9} $ & $ 36.8_{~34.5}^{~37.9} $ \\\\\n",
      " iwcdan_no_trg_ent      & $ 70.1_{~70.0}^{~70.8} $ & $ 50.5_{~49.1}^{~50.8} $ & $ 68.6_{~68.2}^{~69.4} $ & $ 44.2_{~41.2}^{~45.8} $ & $ 66.0_{~65.9}^{~66.0} $ & $ 45.0_{~43.7}^{~47.8} $ & $ 63.8_{~62.3}^{~64.1} $ & $ 37.3_{~33.6}^{~37.7} $ \\\\\n",
      " iwcdan                 & $ 70.1_{~70.1}^{~70.2} $ & $ 47.8_{~42.4}^{~49.3} $ & $ 69.4_{~69.1}^{~69.4} $ & $ 47.1_{~46.3}^{~51.3} $ & $ 66.1_{~65.0}^{~67.2} $ & $ 39.9_{~37.7}^{~40.8} $ & $ 64.5_{~63.9}^{~65.1} $ & $ 37.0_{~35.5}^{~40.2} $ \\\\\n",
      " sdann_4_no_trg_ent     & $ 69.4_{~68.8}^{~70.0} $ & $ 46.5_{~45.1}^{~49.7} $ & $ 69.6_{~69.3}^{~69.7} $ & $ 49.1_{~47.4}^{~49.2} $ & $ 68.0_{~67.8}^{~68.6} $ & $ 48.2_{~42.6}^{~48.8} $ & $ 66.3_{~64.2}^{~66.4} $ & $ 40.7_{~36.6}^{~42.9} $ \\\\\n",
      " sdann_4                & $ 71.8_{~71.7}^{~72.1} $ & $ 52.1_{~52.1}^{~52.8} $ & $ 71.1_{~70.4}^{~71.7} $ & $ 49.9_{~48.1}^{~51.8} $ & $ 69.4_{~68.7}^{~70.0} $ & $ 48.6_{~43.5}^{~49.0} $ & $ 66.4_{~66.2}^{~67.9} $ & $ 39.0_{~33.6}^{~47.1} $ \\\\\n",
      " support_sq_no_trg_ent  & $ 69.9_{~69.9}^{~70.3} $ & $ 48.0_{~46.6}^{~50.1} $ & $ 68.8_{~68.6}^{~68.9} $ & $ 47.3_{~45.3}^{~49.3} $ & $ 68.1_{~67.2}^{~68.7} $ & $ 45.4_{~45.2}^{~47.8} $ & $ 65.7_{~65.6}^{~66.4} $ & $ 43.6_{~41.3}^{~45.0} $ \\\\\n",
      " support_sq             & $ 71.7_{~71.7}^{~71.9} $ & $ 52.9_{~46.7}^{~53.4} $ & $ 70.7_{~70.4}^{~71.0} $ & $ 51.6_{~46.8}^{~52.7} $ & $ 69.2_{~69.2}^{~69.3} $ & $ 45.6_{~43.3}^{~52.0} $ & $ 68.1_{~67.2}^{~68.2} $ & $ 44.7_{~39.8}^{~45.9} $ \\\\\n",
      " support_abs_no_trg_ent & $ 69.8_{~68.9}^{~70.0} $ & $ 45.7_{~45.4}^{~48.0} $ & $ 68.4_{~68.4}^{~68.6} $ & $ 44.3_{~44.0}^{~46.8} $ & $ 67.9_{~67.0}^{~68.1} $ & $ 46.6_{~40.4}^{~48.4} $ & $ 66.3_{~65.7}^{~66.9} $ & $ 41.6_{~40.3}^{~44.9} $ \\\\\n",
      " support_abs            & $ 71.6_{~71.2}^{~71.7} $ & $ 49.0_{~48.4}^{~53.5} $ & $ 70.9_{~70.8}^{~71.0} $ & $ 49.2_{~47.3}^{~50.0} $ & $ 69.6_{~69.6}^{~69.9} $ & $ 43.2_{~42.1}^{~49.5} $ & $ 67.8_{~66.6}^{~68.2} $ & $ 40.9_{~35.4}^{~49.0} $ \\\\\n",
      "\\hline\n",
      "\\end{tabular}\n"
     ]
    }
   ],
   "source": [
    "results.print_table(['eval/target_val/accuracy_class_avg', 'eval/target_val/accuracy_class_min'], ResultsTable.quant_latex_fn,\n",
    "                    tablefmt='latex_raw', sep=' & ')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# STL->CIFAR VADA (0.01) and DANN (1.0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "stl_cifar/test_deep_cnn/seed_[1-5]/s_alpha_00/source_only[^0_]\n",
      "Runs: 5\n",
      "stl_cifar/test_deep_cnn/seed_[1-5]/s_alpha_10/source_only[^0_]\n",
      "Runs: 5\n",
      "stl_cifar/test_deep_cnn/seed_[1-5]/s_alpha_15/source_only[^0_]\n",
      "Runs: 5\n",
      "stl_cifar/test_deep_cnn/seed_[1-5]/s_alpha_20/source_only[^0_]\n",
      "Runs: 5\n",
      "\n",
      "stl_cifar/test_deep_cnn/seed_[1-5]/s_alpha_00/dann_aw_001[^0_]\n",
      "Runs: 5\n",
      "stl_cifar/test_deep_cnn/seed_[1-5]/s_alpha_10/dann_aw_001[^0_]\n",
      "Runs: 5\n",
      "stl_cifar/test_deep_cnn/seed_[1-5]/s_alpha_15/dann_aw_001[^0_]\n",
      "Runs: 5\n",
      "stl_cifar/test_deep_cnn/seed_[1-5]/s_alpha_20/dann_aw_001[^0_]\n",
      "Runs: 5\n",
      "\n",
      "stl_cifar/test_deep_cnn/seed_[1-5]/s_alpha_00/dann[^0_]\n",
      "Runs: 5\n",
      "stl_cifar/test_deep_cnn/seed_[1-5]/s_alpha_10/dann[^0_]\n",
      "Runs: 5\n",
      "stl_cifar/test_deep_cnn/seed_[1-5]/s_alpha_15/dann[^0_]\n",
      "Runs: 5\n",
      "stl_cifar/test_deep_cnn/seed_[1-5]/s_alpha_20/dann[^0_]\n",
      "Runs: 5\n",
      "\n",
      "stl_cifar/test_deep_cnn/seed_[1-5]/s_alpha_00/dann_aw_1[^0_]\n",
      "Runs: 5\n",
      "stl_cifar/test_deep_cnn/seed_[1-5]/s_alpha_10/dann_aw_1[^0_]\n",
      "Runs: 5\n",
      "stl_cifar/test_deep_cnn/seed_[1-5]/s_alpha_15/dann_aw_1[^0_]\n",
      "Runs: 5\n",
      "stl_cifar/test_deep_cnn/seed_[1-5]/s_alpha_20/dann_aw_1[^0_]\n",
      "Runs: 5\n",
      "\n",
      "stl_cifar/test_deep_cnn/seed_[1-5]/s_alpha_00/vada[^0_]\n",
      "Runs: 5\n",
      "stl_cifar/test_deep_cnn/seed_[1-5]/s_alpha_10/vada[^0_]\n",
      "Runs: 5\n",
      "stl_cifar/test_deep_cnn/seed_[1-5]/s_alpha_15/vada[^0_]\n",
      "Runs: 5\n",
      "stl_cifar/test_deep_cnn/seed_[1-5]/s_alpha_20/vada[^0_]\n",
      "Runs: 5\n",
      "\n",
      "stl_cifar/test_deep_cnn/seed_[1-5]/s_alpha_00/vada_001[^0_]\n",
      "Runs: 5\n",
      "stl_cifar/test_deep_cnn/seed_[1-5]/s_alpha_10/vada_001[^0_]\n",
      "Runs: 5\n",
      "stl_cifar/test_deep_cnn/seed_[1-5]/s_alpha_15/vada_001[^0_]\n",
      "Runs: 5\n",
      "stl_cifar/test_deep_cnn/seed_[1-5]/s_alpha_20/vada_001[^0_]\n",
      "Runs: 5\n",
      "\n",
      "stl_cifar/test_deep_cnn/seed_[1-5]/s_alpha_00/sdann_4[^0_]\n",
      "Runs: 5\n",
      "stl_cifar/test_deep_cnn/seed_[1-5]/s_alpha_10/sdann_4[^0_]\n",
      "Runs: 5\n",
      "stl_cifar/test_deep_cnn/seed_[1-5]/s_alpha_15/sdann_4[^0_]\n",
      "Runs: 5\n",
      "stl_cifar/test_deep_cnn/seed_[1-5]/s_alpha_20/sdann_4[^0_]\n",
      "Runs: 5\n",
      "\n",
      "stl_cifar/test_deep_cnn/seed_[1-5]/s_alpha_00/support_sq[^0_]\n",
      "Runs: 5\n",
      "stl_cifar/test_deep_cnn/seed_[1-5]/s_alpha_10/support_sq[^0_]\n",
      "Runs: 5\n",
      "stl_cifar/test_deep_cnn/seed_[1-5]/s_alpha_15/support_sq[^0_]\n",
      "Runs: 5\n",
      "stl_cifar/test_deep_cnn/seed_[1-5]/s_alpha_20/support_sq[^0_]\n",
      "Runs: 5\n",
      "\n",
      "stl_cifar/test_deep_cnn/seed_[1-5]/s_alpha_00/support_sq_vat_trg[^0_]\n",
      "Runs: 5\n",
      "stl_cifar/test_deep_cnn/seed_[1-5]/s_alpha_10/support_sq_vat_trg[^0_]\n",
      "Runs: 5\n",
      "stl_cifar/test_deep_cnn/seed_[1-5]/s_alpha_15/support_sq_vat_trg[^0_]\n",
      "Runs: 5\n",
      "stl_cifar/test_deep_cnn/seed_[1-5]/s_alpha_20/support_sq_vat_trg[^0_]\n",
      "Runs: 5\n",
      "\n",
      "stl_cifar/test_deep_cnn/seed_[1-5]/s_alpha_00/support_sq_vat_trg_aw_001[^0_]\n",
      "Runs: 5\n",
      "stl_cifar/test_deep_cnn/seed_[1-5]/s_alpha_10/support_sq_vat_trg_aw_001[^0_]\n",
      "Runs: 5\n",
      "stl_cifar/test_deep_cnn/seed_[1-5]/s_alpha_15/support_sq_vat_trg_aw_001[^0_]\n",
      "Runs: 5\n",
      "stl_cifar/test_deep_cnn/seed_[1-5]/s_alpha_20/support_sq_vat_trg_aw_001[^0_]\n",
      "Runs: 5\n",
      "\n",
      "stl_cifar/test_deep_cnn/seed_[1-5]/s_alpha_00/support_sq_vat_trg_aw_1[^0_]\n",
      "Runs: 5\n",
      "stl_cifar/test_deep_cnn/seed_[1-5]/s_alpha_10/support_sq_vat_trg_aw_1[^0_]\n",
      "Runs: 5\n",
      "stl_cifar/test_deep_cnn/seed_[1-5]/s_alpha_15/support_sq_vat_trg_aw_1[^0_]\n",
      "Runs: 5\n",
      "stl_cifar/test_deep_cnn/seed_[1-5]/s_alpha_20/support_sq_vat_trg_aw_1[^0_]\n",
      "Runs: 5\n",
      "\n"
     ]
    }
   ],
   "source": [
    "prefix = 'stl_cifar/test_deep_cnn' # change to stl_cifar/deep_cnn\n",
    "imbalance_levels = ['00', '10', '15', '20']\n",
    "algorithms = [\n",
    "    'source_only',    \n",
    "    'dann_aw_001',\n",
    "    'dann',\n",
    "    'dann_aw_1',\n",
    "    'vada',\n",
    "    'vada_001',    \n",
    "    'sdann_4',            \n",
    "    'support_sq',\n",
    "    'support_sq_vat_trg',    \n",
    "    'support_sq_vat_trg_aw_001',\n",
    "    'support_sq_vat_trg_aw_1',    \n",
    "]\n",
    "\n",
    "results = ResultsTable(prefix, algorithms, imbalance_levels, num_steps=40000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "stl_cifar/test_deep_cnn eval/target_val/accuracy_class_avg\n",
      "| algorithm                 | 00                    | 10                    | 15                    | 20                    |\n",
      "|:--------------------------|:----------------------|:----------------------|:----------------------|:----------------------|\n",
      "| source_only               | 69.75 69.88 70.04 [5] | 68.30 68.80 69.25 [5] | 66.39 66.82 67.16 [5] | 64.76 65.84 66.68 [5] |\n",
      "| dann_aw_001               | 72.22 72.25 72.67 [5] | 69.73 70.59 71.15 [5] | 67.21 68.50 68.68 [5] | 64.08 65.87 66.00 [5] |\n",
      "| dann                      | 74.87 75.26 75.38 [5] | 68.60 69.87 70.05 [5] | 63.74 64.93 67.07 [5] | 57.39 63.34 64.78 [5] |\n",
      "| dann_aw_1                 | 76.81 77.23 77.33 [5] | 64.47 66.34 66.82 [5] | 56.14 62.83 63.35 [5] | 52.30 58.69 59.74 [5] |\n",
      "| vada                      | 76.55 76.66 76.72 [5] | 70.02 70.60 71.04 [5] | 65.42 66.07 66.48 [5] | 60.23 63.22 64.72 [5] |\n",
      "| vada_001                  | 74.22 74.37 74.38 [5] | 71.71 71.71 71.71 [5] | 68.43 69.52 69.66 [5] | 64.78 65.93 66.07 [5] |\n",
      "| sdann_4                   | 71.71 71.79 72.11 [5] | 70.42 71.13 71.68 [5] | 68.67 69.40 70.02 [5] | 66.17 66.36 67.92 [5] |\n",
      "| support_sq                | 71.66 71.69 71.93 [5] | 70.43 70.72 70.99 [5] | 69.20 69.22 69.31 [5] | 67.15 68.07 68.18 [5] |\n",
      "| support_sq_vat_trg        | 73.47 73.67 73.97 [5] | 72.08 72.42 72.51 [5] | 70.36 70.63 71.03 [5] | 67.45 67.79 68.43 [5] |\n",
      "| support_sq_vat_trg_aw_001 | 73.14 73.88 74.15 [5] | 72.22 72.24 72.30 [5] | 70.19 70.50 70.77 [5] | 68.03 68.18 68.94 [5] |\n",
      "| support_sq_vat_trg_aw_1   | 74.02 74.20 74.47 [5] | 71.91 72.19 72.22 [5] | 70.43 70.63 70.83 [5] | 66.84 67.38 67.71 [5] |\n"
     ]
    }
   ],
   "source": [
    "results.print_table(['eval/target_val/accuracy_class_avg'], ResultsTable.quant_fn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "stl_cifar/test_deep_cnn eval/target_val/accuracy_class_min\n",
      "| algorithm                 | 00                    | 10                    | 15                    | 20                    |\n",
      "|:--------------------------|:----------------------|:----------------------|:----------------------|:----------------------|\n",
      "| source_only               | 45.30 49.79 50.65 [5] | 45.35 47.21 48.15 [5] | 45.76 46.04 47.02 [5] | 41.58 43.74 44.65 [5] |\n",
      "| dann_aw_001               | 48.75 49.47 50.82 [5] | 41.49 48.88 51.25 [5] | 36.24 46.11 50.05 [5] | 29.90 36.67 39.41 [5] |\n",
      "| dann                      | 54.25 54.61 56.63 [5] | 40.69 44.75 45.05 [5] | 33.95 34.87 36.83 [5] | 21.19 27.04 28.47 [5] |\n",
      "| dann_aw_1                 | 56.69 58.46 59.44 [5] | 37.45 37.92 41.58 [5] | 24.61 27.55 28.92 [5] | 17.15 18.53 20.53 [5] |\n",
      "| vada                      | 53.50 56.89 58.32 [5] | 43.96 47.67 48.77 [5] | 33.27 35.74 39.31 [5] | 25.19 25.54 27.98 [5] |\n",
      "| vada_001                  | 52.59 54.22 55.38 [5] | 44.95 51.65 52.05 [5] | 40.00 47.47 49.75 [5] | 35.35 37.16 39.41 [5] |\n",
      "| sdann_4                   | 52.06 52.15 52.76 [5] | 48.12 49.85 51.84 [5] | 43.45 48.55 49.04 [5] | 33.56 39.01 47.05 [5] |\n",
      "| support_sq                | 46.65 52.87 53.44 [5] | 46.75 51.65 52.75 [5] | 43.27 45.61 52.04 [5] | 39.76 44.65 45.85 [5] |\n",
      "| support_sq_vat_trg        | 53.05 53.96 54.22 [5] | 51.12 53.85 53.94 [5] | 46.14 51.55 53.58 [5] | 35.40 38.02 48.05 [5] |\n",
      "| support_sq_vat_trg_aw_001 | 53.47 54.45 54.74 [5] | 47.33 52.34 55.72 [5] | 45.88 46.52 49.75 [5] | 34.36 37.92 46.66 [5] |\n",
      "| support_sq_vat_trg_aw_1   | 51.88 52.18 52.47 [5] | 45.45 53.55 53.64 [5] | 45.64 48.85 52.35 [5] | 39.40 42.97 46.01 [5] |\n"
     ]
    }
   ],
   "source": [
    "results.print_table(['eval/target_val/accuracy_class_min'], ResultsTable.quant_fn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "stl_cifar/test_deep_cnn eval/target_val/accuracy_class_avg eval/target_val/accuracy_class_min\n",
      "| algorithm                 | 00                                                | 10                                                | 15                                                | 20                                                |\n",
      "|:--------------------------|:--------------------------------------------------|:--------------------------------------------------|:--------------------------------------------------|:--------------------------------------------------|\n",
      "| source_only               | $ 69.9_{~69.8}^{~70.0} $ $ 49.8_{~45.3}^{~50.6} $ | $ 68.8_{~68.3}^{~69.3} $ $ 47.2_{~45.3}^{~48.2} $ | $ 66.8_{~66.4}^{~67.2} $ $ 46.0_{~45.8}^{~47.0} $ | $ 65.8_{~64.8}^{~66.7} $ $ 43.7_{~41.6}^{~44.6} $ |\n",
      "| dann_aw_001               | $ 72.3_{~72.2}^{~72.7} $ $ 49.5_{~48.8}^{~50.8} $ | $ 70.6_{~69.7}^{~71.2} $ $ 48.9_{~41.5}^{~51.2} $ | $ 68.5_{~67.2}^{~68.7} $ $ 46.1_{~36.2}^{~50.0} $ | $ 65.9_{~64.1}^{~66.0} $ $ 36.7_{~29.9}^{~39.4} $ |\n",
      "| dann                      | $ 75.3_{~74.9}^{~75.4} $ $ 54.6_{~54.2}^{~56.6} $ | $ 69.9_{~68.6}^{~70.1} $ $ 44.8_{~40.7}^{~45.1} $ | $ 64.9_{~63.7}^{~67.1} $ $ 34.9_{~33.9}^{~36.8} $ | $ 63.3_{~57.4}^{~64.8} $ $ 27.0_{~21.2}^{~28.5} $ |\n",
      "| dann_aw_1                 | $ 77.2_{~76.8}^{~77.3} $ $ 58.5_{~56.7}^{~59.4} $ | $ 66.3_{~64.5}^{~66.8} $ $ 37.9_{~37.5}^{~41.6} $ | $ 62.8_{~56.1}^{~63.3} $ $ 27.5_{~24.6}^{~28.9} $ | $ 58.7_{~52.3}^{~59.7} $ $ 18.5_{~17.2}^{~20.5} $ |\n",
      "| vada                      | $ 76.7_{~76.6}^{~76.7} $ $ 56.9_{~53.5}^{~58.3} $ | $ 70.6_{~70.0}^{~71.0} $ $ 47.7_{~44.0}^{~48.8} $ | $ 66.1_{~65.4}^{~66.5} $ $ 35.7_{~33.3}^{~39.3} $ | $ 63.2_{~60.2}^{~64.7} $ $ 25.5_{~25.2}^{~28.0} $ |\n",
      "| vada_001                  | $ 74.4_{~74.2}^{~74.4} $ $ 54.2_{~52.6}^{~55.4} $ | $ 71.7_{~71.7}^{~71.7} $ $ 51.6_{~45.0}^{~52.0} $ | $ 69.5_{~68.4}^{~69.7} $ $ 47.5_{~40.0}^{~49.8} $ | $ 65.9_{~64.8}^{~66.1} $ $ 37.2_{~35.3}^{~39.4} $ |\n",
      "| sdann_4                   | $ 71.8_{~71.7}^{~72.1} $ $ 52.1_{~52.1}^{~52.8} $ | $ 71.1_{~70.4}^{~71.7} $ $ 49.9_{~48.1}^{~51.8} $ | $ 69.4_{~68.7}^{~70.0} $ $ 48.6_{~43.5}^{~49.0} $ | $ 66.4_{~66.2}^{~67.9} $ $ 39.0_{~33.6}^{~47.1} $ |\n",
      "| support_sq                | $ 71.7_{~71.7}^{~71.9} $ $ 52.9_{~46.7}^{~53.4} $ | $ 70.7_{~70.4}^{~71.0} $ $ 51.6_{~46.8}^{~52.7} $ | $ 69.2_{~69.2}^{~69.3} $ $ 45.6_{~43.3}^{~52.0} $ | $ 68.1_{~67.2}^{~68.2} $ $ 44.7_{~39.8}^{~45.9} $ |\n",
      "| support_sq_vat_trg        | $ 73.7_{~73.5}^{~74.0} $ $ 54.0_{~53.0}^{~54.2} $ | $ 72.4_{~72.1}^{~72.5} $ $ 53.8_{~51.1}^{~53.9} $ | $ 70.6_{~70.4}^{~71.0} $ $ 51.5_{~46.1}^{~53.6} $ | $ 67.8_{~67.4}^{~68.4} $ $ 38.0_{~35.4}^{~48.1} $ |\n",
      "| support_sq_vat_trg_aw_001 | $ 73.9_{~73.1}^{~74.2} $ $ 54.4_{~53.5}^{~54.7} $ | $ 72.2_{~72.2}^{~72.3} $ $ 52.3_{~47.3}^{~55.7} $ | $ 70.5_{~70.2}^{~70.8} $ $ 46.5_{~45.9}^{~49.8} $ | $ 68.2_{~68.0}^{~68.9} $ $ 37.9_{~34.4}^{~46.7} $ |\n",
      "| support_sq_vat_trg_aw_1   | $ 74.2_{~74.0}^{~74.5} $ $ 52.2_{~51.9}^{~52.5} $ | $ 72.2_{~71.9}^{~72.2} $ $ 53.5_{~45.4}^{~53.6} $ | $ 70.6_{~70.4}^{~70.8} $ $ 48.9_{~45.6}^{~52.3} $ | $ 67.4_{~66.8}^{~67.7} $ $ 43.0_{~39.4}^{~46.0} $ |\n"
     ]
    }
   ],
   "source": [
    "results.print_table(['eval/target_val/accuracy_class_avg', 'eval/target_val/accuracy_class_min'], ResultsTable.quant_latex_fn)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "| Algorithm   | $\\lambda_{\\text{align}}$   | $\\alpha = 0.0$                                    | $\\alpha = 1.0$                                | $\\alpha = 1.5$                                         | $\\alpha = 2.0$                                    |\n",
    "|:------------|:-----------------------|:--------------------------------------------------|:--------------------------------------------------|:--------------------------------------------------|:--------------------------------------------------|\n",
    "| DANN        | $0.1$                     | $ 75.3_{74.9}^{75.4} $ $ 54.6_{54.2}^{56.6} $ | $ 69.9_{68.6}^{70.1} $ $ 44.8_{40.7}^{45.1} $ | $ 64.9_{63.7}^{67.1} $ $ 34.9_{33.9}^{36.8} $ | $ 63.3_{~57.4}^{64.8} $ $ 27.0_{21.2}^{28.5} $ |\n",
    "| DANN        | $1.0$                     | $ 77.2_{76.8}^{77.3} $ $ 58.5_{56.7}^{59.4} $ | $ 66.3_{64.5}^{66.8} $ $ 37.9_{37.5}^{41.6} $ | $ 62.8_{56.1}^{63.3} $ $ 27.5_{24.6}^{28.9} $ | $ 58.7_{~52.3}^{59.7} $ $ 18.5_{17.2}^{20.5} $ |\n",
    "| VADA        | $0.1$                     | $ 76.7_{76.6}^{76.7} $ $ 56.9_{53.5}^{58.3} $ | $ 70.6_{70.0}^{71.0} $ $ 47.7_{44.0}^{48.8} $ | $ 66.1_{65.4}^{66.5} $ $ 35.7_{33.3}^{39.3} $ | $ 63.2_{~60.2}^{64.7} $ $ 25.5_{25.2}^{28.0} $ |\n",
    "| VADA        | $0.01$                    | $ 74.4_{74.2}^{74.4} $ $ 54.2_{52.6}^{55.4} $ | $ 71.7_{71.7}^{71.7} $ $ 51.6_{45.0}^{52.0} $ | $ 69.5_{68.4}^{69.7} $ $ 47.5_{40.0}^{49.8} $ | $ 65.9_{~64.8}^{66.1} $ $ 37.2_{35.3}^{39.4} $ |\n",
    "| ASA-sq       | $0.1$                     | $ 71.7_{71.7}^{71.9} $ $ 52.9_{46.7}^{53.4} $ | $ 70.7_{70.4}^{71.0} $ $ 51.6_{46.8}^{52.7} $ | $ 69.2_{69.2}^{69.3} $ $ 45.6_{43.3}^{52.0} $ | $ 68.1_{~67.2}^{68.2} $ $ 44.7_{39.8}^{45.9} $ |\n",
    "| ASA-sq + VAT | $1.0$                     | $ 74.2_{74.0}^{74.5} $ $ 52.2_{51.9}^{52.5} $ | $ 72.2_{71.9}^{72.2} $ $ 53.5_{45.4}^{53.6} $ | $ 70.6_{70.4}^{70.8} $ $ 48.9_{45.6}^{52.3} $ | $ 67.4_{66.8}^{67.7} $ $ 43.0_{39.4}^{46.0} $ |"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "stl_cifar/test_deep_cnn eval/target_val/accuracy_class_avg eval/target_val/accuracy_class_min\n",
      "\\begin{tabular}{lllll}\n",
      "\\hline\n",
      " algorithm                 & 00                                                  & 10                                                  & 15                                                  & 20                                                  \\\\\n",
      "\\hline\n",
      " source_only               & $ 69.9_{~69.8}^{~70.0} $ & $ 49.8_{~45.3}^{~50.6} $ & $ 68.8_{~68.3}^{~69.3} $ & $ 47.2_{~45.3}^{~48.2} $ & $ 66.8_{~66.4}^{~67.2} $ & $ 46.0_{~45.8}^{~47.0} $ & $ 65.8_{~64.8}^{~66.7} $ & $ 43.7_{~41.6}^{~44.6} $ \\\\\n",
      " dann_aw_001               & $ 72.3_{~72.2}^{~72.7} $ & $ 49.5_{~48.8}^{~50.8} $ & $ 70.6_{~69.7}^{~71.2} $ & $ 48.9_{~41.5}^{~51.2} $ & $ 68.5_{~67.2}^{~68.7} $ & $ 46.1_{~36.2}^{~50.0} $ & $ 65.9_{~64.1}^{~66.0} $ & $ 36.7_{~29.9}^{~39.4} $ \\\\\n",
      " dann                      & $ 75.3_{~74.9}^{~75.4} $ & $ 54.6_{~54.2}^{~56.6} $ & $ 69.9_{~68.6}^{~70.1} $ & $ 44.8_{~40.7}^{~45.1} $ & $ 64.9_{~63.7}^{~67.1} $ & $ 34.9_{~33.9}^{~36.8} $ & $ 63.3_{~57.4}^{~64.8} $ & $ 27.0_{~21.2}^{~28.5} $ \\\\\n",
      " dann_aw_1                 & $ 77.2_{~76.8}^{~77.3} $ & $ 58.5_{~56.7}^{~59.4} $ & $ 66.3_{~64.5}^{~66.8} $ & $ 37.9_{~37.5}^{~41.6} $ & $ 62.8_{~56.1}^{~63.3} $ & $ 27.5_{~24.6}^{~28.9} $ & $ 58.7_{~52.3}^{~59.7} $ & $ 18.5_{~17.2}^{~20.5} $ \\\\\n",
      " vada                      & $ 76.7_{~76.6}^{~76.7} $ & $ 56.9_{~53.5}^{~58.3} $ & $ 70.6_{~70.0}^{~71.0} $ & $ 47.7_{~44.0}^{~48.8} $ & $ 66.1_{~65.4}^{~66.5} $ & $ 35.7_{~33.3}^{~39.3} $ & $ 63.2_{~60.2}^{~64.7} $ & $ 25.5_{~25.2}^{~28.0} $ \\\\\n",
      " vada_001                  & $ 74.4_{~74.2}^{~74.4} $ & $ 54.2_{~52.6}^{~55.4} $ & $ 71.7_{~71.7}^{~71.7} $ & $ 51.6_{~45.0}^{~52.0} $ & $ 69.5_{~68.4}^{~69.7} $ & $ 47.5_{~40.0}^{~49.8} $ & $ 65.9_{~64.8}^{~66.1} $ & $ 37.2_{~35.3}^{~39.4} $ \\\\\n",
      " sdann_4                   & $ 71.8_{~71.7}^{~72.1} $ & $ 52.1_{~52.1}^{~52.8} $ & $ 71.1_{~70.4}^{~71.7} $ & $ 49.9_{~48.1}^{~51.8} $ & $ 69.4_{~68.7}^{~70.0} $ & $ 48.6_{~43.5}^{~49.0} $ & $ 66.4_{~66.2}^{~67.9} $ & $ 39.0_{~33.6}^{~47.1} $ \\\\\n",
      " support_sq                & $ 71.7_{~71.7}^{~71.9} $ & $ 52.9_{~46.7}^{~53.4} $ & $ 70.7_{~70.4}^{~71.0} $ & $ 51.6_{~46.8}^{~52.7} $ & $ 69.2_{~69.2}^{~69.3} $ & $ 45.6_{~43.3}^{~52.0} $ & $ 68.1_{~67.2}^{~68.2} $ & $ 44.7_{~39.8}^{~45.9} $ \\\\\n",
      " support_sq_vat_trg        & $ 73.7_{~73.5}^{~74.0} $ & $ 54.0_{~53.0}^{~54.2} $ & $ 72.4_{~72.1}^{~72.5} $ & $ 53.8_{~51.1}^{~53.9} $ & $ 70.6_{~70.4}^{~71.0} $ & $ 51.5_{~46.1}^{~53.6} $ & $ 67.8_{~67.4}^{~68.4} $ & $ 38.0_{~35.4}^{~48.1} $ \\\\\n",
      " support_sq_vat_trg_aw_001 & $ 73.9_{~73.1}^{~74.2} $ & $ 54.4_{~53.5}^{~54.7} $ & $ 72.2_{~72.2}^{~72.3} $ & $ 52.3_{~47.3}^{~55.7} $ & $ 70.5_{~70.2}^{~70.8} $ & $ 46.5_{~45.9}^{~49.8} $ & $ 68.2_{~68.0}^{~68.9} $ & $ 37.9_{~34.4}^{~46.7} $ \\\\\n",
      " support_sq_vat_trg_aw_1   & $ 74.2_{~74.0}^{~74.5} $ & $ 52.2_{~51.9}^{~52.5} $ & $ 72.2_{~71.9}^{~72.2} $ & $ 53.5_{~45.4}^{~53.6} $ & $ 70.6_{~70.4}^{~70.8} $ & $ 48.9_{~45.6}^{~52.3} $ & $ 67.4_{~66.8}^{~67.7} $ & $ 43.0_{~39.4}^{~46.0} $ \\\\\n",
      "\\hline\n",
      "\\end{tabular}\n"
     ]
    }
   ],
   "source": [
    "results.print_table(['eval/target_val/accuracy_class_avg', 'eval/target_val/accuracy_class_min'], \n",
    "                    ResultsTable.quant_latex_fn, tablefmt='latex_raw', sep=' & ')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# VisDA17 ResNet50"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "visda17/test_resnet50_v3/seed_[1-5]/s_alpha_00/source_only[^0_]\n",
      "Runs: 5\n",
      "visda17/test_resnet50_v3/seed_[1-5]/s_alpha_10/source_only[^0_]\n",
      "Runs: 5\n",
      "visda17/test_resnet50_v3/seed_[1-5]/s_alpha_15/source_only[^0_]\n",
      "Runs: 5\n",
      "visda17/test_resnet50_v3/seed_[1-5]/s_alpha_20/source_only[^0_]\n",
      "Runs: 5\n",
      "\n",
      "visda17/test_resnet50_v3/seed_[1-5]/s_alpha_00/dann[^0_]\n",
      "Runs: 5\n",
      "visda17/test_resnet50_v3/seed_[1-5]/s_alpha_10/dann[^0_]\n",
      "Runs: 5\n",
      "visda17/test_resnet50_v3/seed_[1-5]/s_alpha_15/dann[^0_]\n",
      "Runs: 5\n",
      "visda17/test_resnet50_v3/seed_[1-5]/s_alpha_20/dann[^0_]\n",
      "Runs: 5\n",
      "\n",
      "visda17/test_resnet50_v3/seed_[1-5]/s_alpha_00/iwdan[^0_]\n",
      "Runs: 5\n",
      "visda17/test_resnet50_v3/seed_[1-5]/s_alpha_10/iwdan[^0_]\n",
      "Runs: 5\n",
      "visda17/test_resnet50_v3/seed_[1-5]/s_alpha_15/iwdan[^0_]\n",
      "Runs: 5\n",
      "visda17/test_resnet50_v3/seed_[1-5]/s_alpha_20/iwdan[^0_]\n",
      "Runs: 5\n",
      "\n",
      "visda17/test_resnet50_v3/seed_[1-5]/s_alpha_00/iwcdan[^0_]\n",
      "Runs: 5\n",
      "visda17/test_resnet50_v3/seed_[1-5]/s_alpha_10/iwcdan[^0_]\n",
      "Runs: 5\n",
      "visda17/test_resnet50_v3/seed_[1-5]/s_alpha_15/iwcdan[^0_]\n",
      "Runs: 5\n",
      "visda17/test_resnet50_v3/seed_[1-5]/s_alpha_20/iwcdan[^0_]\n",
      "Runs: 5\n",
      "\n",
      "visda17/test_resnet50_v3/seed_[1-5]/s_alpha_00/sdann_4[^0_]\n",
      "Runs: 5\n",
      "visda17/test_resnet50_v3/seed_[1-5]/s_alpha_10/sdann_4[^0_]\n",
      "Runs: 5\n",
      "visda17/test_resnet50_v3/seed_[1-5]/s_alpha_15/sdann_4[^0_]\n",
      "Runs: 5\n",
      "visda17/test_resnet50_v3/seed_[1-5]/s_alpha_20/sdann_4[^0_]\n",
      "Runs: 5\n",
      "\n",
      "visda17/test_resnet50_v3/seed_[1-5]/s_alpha_00/support_sq[^0_]\n",
      "Runs: 5\n",
      "visda17/test_resnet50_v3/seed_[1-5]/s_alpha_10/support_sq[^0_]\n",
      "Runs: 5\n",
      "visda17/test_resnet50_v3/seed_[1-5]/s_alpha_15/support_sq[^0_]\n",
      "Runs: 5\n",
      "visda17/test_resnet50_v3/seed_[1-5]/s_alpha_20/support_sq[^0_]\n",
      "Runs: 5\n",
      "\n",
      "visda17/test_resnet50_v3/seed_[1-5]/s_alpha_00/support_abs[^0_]\n",
      "Runs: 5\n",
      "visda17/test_resnet50_v3/seed_[1-5]/s_alpha_10/support_abs[^0_]\n",
      "Runs: 5\n",
      "visda17/test_resnet50_v3/seed_[1-5]/s_alpha_15/support_abs[^0_]\n",
      "Runs: 5\n",
      "visda17/test_resnet50_v3/seed_[1-5]/s_alpha_20/support_abs[^0_]\n",
      "Runs: 5\n",
      "\n",
      "visda17/test_resnet50_v3/seed_[1-5]/s_alpha_00/vada_vat_src_1[^0_]\n",
      "Runs: 5\n",
      "visda17/test_resnet50_v3/seed_[1-5]/s_alpha_10/vada_vat_src_1[^0_]\n",
      "Runs: 5\n",
      "visda17/test_resnet50_v3/seed_[1-5]/s_alpha_15/vada_vat_src_1[^0_]\n",
      "Runs: 5\n",
      "visda17/test_resnet50_v3/seed_[1-5]/s_alpha_20/vada_vat_src_1[^0_]\n",
      "Runs: 5\n",
      "\n"
     ]
    }
   ],
   "source": [
    "prefix = 'visda17/test_resnet50_v3' # change to visda17/resnet50\n",
    "imbalance_levels = ['00', '10', '15', '20']\n",
    "algorithms = [\n",
    "    'source_only',\n",
    "    'dann',        \n",
    "    'iwdan',\n",
    "    'iwcdan', \n",
    "    'sdann_4',\n",
    "    'support_sq',\n",
    "    'support_abs',\n",
    "    'vada_vat_src_1',\n",
    "]\n",
    "results = ResultsTable(prefix, algorithms, imbalance_levels, num_steps=50000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "visda17/test_resnet50_v3 eval/target_val/accuracy_class_avg\n",
      "| algorithm      | 00               | 10               | 15               | 20               |\n",
      "|:---------------|:-----------------|:-----------------|:-----------------|:-----------------|\n",
      "| source_only    | 49.76 (0.70) [5] | 49.77 (1.33) [5] | 47.26 (1.13) [5] | 45.95 (1.62) [5] |\n",
      "| dann           | 75.91 (1.89) [5] | 64.60 (3.46) [5] | 52.63 (6.34) [5] | 44.17 (5.93) [5] |\n",
      "| iwdan          | 72.53 (1.36) [5] | 64.48 (3.68) [5] | 55.09 (6.49) [5] | 47.00 (7.51) [5] |\n",
      "| iwcdan         | 72.63 (2.33) [5] | 61.81 (4.54) [5] | 50.94 (7.18) [5] | 43.28 (9.08) [5] |\n",
      "| sdann_4        | 72.57 (0.85) [5] | 67.28 (2.35) [5] | 58.31 (4.56) [5] | 52.50 (6.10) [5] |\n",
      "| support_sq     | 64.50 (1.00) [5] | 61.60 (2.26) [5] | 57.76 (2.77) [5] | 53.01 (3.95) [5] |\n",
      "| support_abs    | 64.51 (0.79) [5] | 61.52 (1.70) [5] | 58.40 (3.69) [5] | 55.14 (4.56) [5] |\n",
      "| vada_vat_src_1 | 75.76 (1.46) [5] | 64.26 (3.33) [5] | 53.49 (5.50) [5] | 45.38 (6.33) [5] |\n"
     ]
    }
   ],
   "source": [
    "results.print_table(['eval/target_val/accuracy_class_avg'], ResultsTable.mean_std_fn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "visda17/test_resnet50_v3 eval/target_val/accuracy_class_avg\n",
      "| algorithm      | 00                    | 10                    | 15                    | 20                    |\n",
      "|:---------------|:----------------------|:----------------------|:----------------------|:----------------------|\n",
      "| source_only    | 49.39 49.52 50.53 [5] | 49.21 50.18 50.82 [5] | 46.63 47.06 47.59 [5] | 45.16 45.32 46.53 [5] |\n",
      "| dann           | 74.41 75.42 76.19 [5] | 62.82 64.10 65.29 [5] | 51.45 52.11 52.33 [5] | 39.11 43.14 44.34 [5] |\n",
      "| iwdan          | 72.89 73.16 73.32 [5] | 61.12 64.38 64.63 [5] | 51.03 51.32 56.63 [5] | 41.70 45.08 47.98 [5] |\n",
      "| iwcdan         | 70.63 71.60 75.19 [5] | 60.22 60.65 61.00 [5] | 45.61 49.67 51.89 [5] | 37.29 38.28 46.25 [5] |\n",
      "| sdann_4        | 71.80 72.35 73.28 [5] | 66.19 68.37 68.67 [5] | 56.79 57.24 57.76 [5] | 49.84 50.74 51.66 [5] |\n",
      "| support_sq     | 63.71 64.87 64.99 [5] | 60.55 61.77 63.15 [5] | 55.45 57.81 58.33 [5] | 50.76 51.91 52.03 [5] |\n",
      "| support_abs    | 64.49 64.77 65.03 [5] | 60.46 62.03 62.26 [5] | 56.20 57.08 58.37 [5] | 51.88 52.50 56.60 [5] |\n",
      "| vada_vat_src_1 | 74.77 75.28 76.03 [5] | 61.16 64.60 65.07 [5] | 51.57 53.02 54.16 [5] | 40.92 43.90 44.71 [5] |\n"
     ]
    }
   ],
   "source": [
    "results.print_table(['eval/target_val/accuracy_class_avg'], ResultsTable.quant_fn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "visda17/test_resnet50_v3 eval/target_val/accuracy_class_min\n",
      "| algorithm      | 00               | 10                | 15               | 20               |\n",
      "|:---------------|:-----------------|:------------------|:-----------------|:-----------------|\n",
      "| source_only    | 22.64 (2.56) [5] | 20.65 (1.59) [5]  | 20.25 (2.69) [5] | 17.36 (3.23) [5] |\n",
      "| dann           | 38.01 (2.59) [5] | 26.19 (3.45) [5]  | 12.36 (3.69) [5] | 8.74 (6.82) [5]  |\n",
      "| iwdan          | 29.44 (6.02) [5] | 15.11 (11.02) [5] | 8.38 (8.91) [5]  | 7.98 (7.47) [5]  |\n",
      "| iwcdan         | 26.50 (4.41) [5] | 5.80 (6.52) [5]   | 6.42 (9.06) [5]  | 2.71 (4.14) [5]  |\n",
      "| sdann_4        | 37.81 (5.15) [5] | 29.76 (8.42) [5]  | 20.08 (4.66) [5] | 18.08 (5.40) [5] |\n",
      "| support_sq     | 33.49 (5.07) [5] | 27.53 (10.48) [5] | 25.88 (9.93) [5] | 20.76 (6.58) [5] |\n",
      "| support_abs    | 37.70 (6.22) [5] | 25.20 (8.70) [5]  | 24.74 (9.77) [5] | 21.21 (6.35) [5] |\n",
      "| vada_vat_src_1 | 40.46 (2.78) [5] | 24.68 (4.05) [5]  | 15.36 (6.45) [5] | 9.39 (5.39) [5]  |\n"
     ]
    }
   ],
   "source": [
    "results.print_table(['eval/target_val/accuracy_class_min'], ResultsTable.mean_std_fn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "visda17/test_resnet50_v3 eval/target_val/accuracy_class_min\n",
      "| algorithm      | 00                    | 10                    | 15                    | 20                    |\n",
      "|:---------------|:----------------------|:----------------------|:----------------------|:----------------------|\n",
      "| source_only    | 22.16 22.24 24.56 [5] | 20.67 21.21 21.26 [5] | 18.56 18.57 22.24 [5] | 14.37 19.51 19.83 [5] |\n",
      "| dann           | 35.60 36.73 40.89 [5] | 24.83 25.05 29.34 [5] | 11.38 11.52 12.38 [5] | 03.57 03.58 14.32 [5] |\n",
      "| iwdan          | 22.81 31.73 34.81 [5] | 05.02 12.05 24.65 [5] | 02.15 04.59 10.35 [5] | 01.17 04.59 13.58 [5] |\n",
      "| iwcdan         | 22.80 27.59 28.00 [5] | 00.75 01.09 11.28 [5] | 00.19 02.18 05.66 [5] | 00.32 00.55 01.75 [5] |\n",
      "| sdann_4        | 32.34 37.83 40.78 [5] | 26.19 26.55 29.39 [5] | 16.69 18.56 23.94 [5] | 17.11 18.56 20.00 [5] |\n",
      "| support_sq     | 32.07 35.72 35.76 [5] | 20.41 31.38 34.40 [5] | 17.35 26.67 32.08 [5] | 16.86 18.26 21.15 [5] |\n",
      "| support_abs    | 35.97 40.60 41.89 [5] | 16.67 27.27 29.73 [5] | 13.95 25.97 31.21 [5] | 17.68 19.73 22.21 [5] |\n",
      "| vada_vat_src_1 | 39.75 40.51 41.83 [5] | 21.72 22.75 28.17 [5] | 13.74 14.84 21.73 [5] | 04.98 08.50 11.07 [5] |\n"
     ]
    }
   ],
   "source": [
    "results.print_table(['eval/target_val/accuracy_class_min'], ResultsTable.quant_fn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "visda17/test_resnet50_v3 eval/target_val/accuracy_class_avg eval/target_val/accuracy_class_min\n",
      "\\begin{tabular}{lllll}\n",
      "\\hline\n",
      " algorithm      & 00                                                  & 10                                                  & 15                                                  & 20                                                  \\\\\n",
      "\\hline\n",
      " source_only    & $ 49.5_{~49.4}^{~50.5} $ & $ 22.2_{~22.2}^{~24.6} $ & $ 50.2_{~49.2}^{~50.8} $ & $ 21.2_{~20.7}^{~21.3} $ & $ 47.1_{~46.6}^{~47.6} $ & $ 18.6_{~18.6}^{~22.2} $ & $ 45.3_{~45.2}^{~46.5} $ & $ 19.5_{~14.4}^{~19.8} $ \\\\\n",
      " dann           & $ 75.4_{~74.4}^{~76.2} $ & $ 36.7_{~35.6}^{~40.9} $ & $ 64.1_{~62.8}^{~65.3} $ & $ 25.0_{~24.8}^{~29.3} $ & $ 52.1_{~51.4}^{~52.3} $ & $ 11.5_{~11.4}^{~12.4} $ & $ 43.1_{~39.1}^{~44.3} $ & $ 03.6_{~03.6}^{~14.3} $ \\\\\n",
      " iwdan          & $ 73.2_{~72.9}^{~73.3} $ & $ 31.7_{~22.8}^{~34.8} $ & $ 64.4_{~61.1}^{~64.6} $ & $ 12.1_{~05.0}^{~24.7} $ & $ 51.3_{~51.0}^{~56.6} $ & $ 04.6_{~02.1}^{~10.4} $ & $ 45.1_{~41.7}^{~48.0} $ & $ 04.6_{~01.2}^{~13.6} $ \\\\\n",
      " iwcdan         & $ 71.6_{~70.6}^{~75.2} $ & $ 27.6_{~22.8}^{~28.0} $ & $ 60.6_{~60.2}^{~61.0} $ & $ 01.1_{~00.7}^{~11.3} $ & $ 49.7_{~45.6}^{~51.9} $ & $ 02.2_{~00.2}^{~05.7} $ & $ 38.3_{~37.3}^{~46.2} $ & $ 00.6_{~00.3}^{~01.7} $ \\\\\n",
      " sdann_4        & $ 72.4_{~71.8}^{~73.3} $ & $ 37.8_{~32.3}^{~40.8} $ & $ 68.4_{~66.2}^{~68.7} $ & $ 26.6_{~26.2}^{~29.4} $ & $ 57.2_{~56.8}^{~57.8} $ & $ 18.6_{~16.7}^{~23.9} $ & $ 50.7_{~49.8}^{~51.7} $ & $ 18.6_{~17.1}^{~20.0} $ \\\\\n",
      " support_sq     & $ 64.9_{~63.7}^{~65.0} $ & $ 35.7_{~32.1}^{~35.8} $ & $ 61.8_{~60.6}^{~63.2} $ & $ 31.4_{~20.4}^{~34.4} $ & $ 57.8_{~55.5}^{~58.3} $ & $ 26.7_{~17.3}^{~32.1} $ & $ 51.9_{~50.8}^{~52.0} $ & $ 18.3_{~16.9}^{~21.2} $ \\\\\n",
      " support_abs    & $ 64.8_{~64.5}^{~65.0} $ & $ 40.6_{~36.0}^{~41.9} $ & $ 62.0_{~60.5}^{~62.3} $ & $ 27.3_{~16.7}^{~29.7} $ & $ 57.1_{~56.2}^{~58.4} $ & $ 26.0_{~13.9}^{~31.2} $ & $ 52.5_{~51.9}^{~56.6} $ & $ 19.7_{~17.7}^{~22.2} $ \\\\\n",
      " vada_vat_src_1 & $ 75.3_{~74.8}^{~76.0} $ & $ 40.5_{~39.7}^{~41.8} $ & $ 64.6_{~61.2}^{~65.1} $ & $ 22.8_{~21.7}^{~28.2} $ & $ 53.0_{~51.6}^{~54.2} $ & $ 14.8_{~13.7}^{~21.7} $ & $ 43.9_{~40.9}^{~44.7} $ & $ 08.5_{~05.0}^{~11.1} $ \\\\\n",
      "\\hline\n",
      "\\end{tabular}\n"
     ]
    }
   ],
   "source": [
    "results.print_table(['eval/target_val/accuracy_class_avg', 'eval/target_val/accuracy_class_min'], \n",
    "                    ResultsTable.quant_latex_fn, tablefmt='latex_raw', sep=' & ')"
   ]
  }
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